Computer-based systems employing a network of sensors to support the storage and/or transport of various goods and methods of use thereof to manage losses from quality shortfall

ABSTRACT

In some embodiments, the present disclosure provides a network of multi-functional sensors; where, based on a quality insurance, each multi-functional sensor is positioned in, on, or in a vicinity of: a transported cargo and/or a cargo container, containing the transported cargo; where each multi-functional sensor is configured to measure particular transport-related condition, particular cargo-related condition, or both, to form cargo transport sensor data and wirelessly transmit it to a server that is configured to dynamically predict, based on the cargo transport sensor data, a predicted quality loss of the transported cargo, determine a current loss value of the transported cargo and cause one or more remedial actions that include instantaneously instructing to pay a payout amount to an owner of the transported cargo to compensate for the current loss value and/or transmitting a remedial instruction with an adjustment to the operation of one or more of a cargo transport, the cargo container, and a cargo storage.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from U.S. Provisional PatentApplication No. Provisional Appl. No. 62/571,975, entitled“COMPUTER-BASED SYSTEMS EMPLOYING A NETWORK OF SENSORS TO SUPPORT THETRANSPORT OF VARIOUS GOODS AND METHODS OF USE THEREOF TO MANAGE LOSSESFROM QUALITY SHORTFALL DURING THE TRANSPORT,” and filed Oct. 13, 2017,which is incorporated herein by reference in its entirety for allpurposes.

BACKGROUND OF THE INVENTION

On occasion, cargo storage and/or transport may result in property lossfrom numerous perils such as, without limitation, perils related toweather, thief, mishandling, environmental stresses, equipmentmalfunction, fire, and/or any other potential quality-affectingevent(s). For example, transporting cargo is a highly complex andnontransparent industry with many isolated processes for eachstakeholder in the value chain. For example, there are numeroustechnological problems that have been associated with transporting andstoring the cargo, such as but not limited to perishable goods,antiques, pharmaceuticals, automobile parts, computer parts, etc. Asdetailed herein, in at least some of embodiments, the term cargotransport and its derivatives includes all activities (includingin-transit storage, etc.) and all parties from a time the cargo leaves amanufacturer's site and/or seller's site to a delivery place asidentified, for example, in cargo transport documents. As detailedherein, while may embodiments herein have been described with respectthe cargo transport, a person skilled in the art that the presentdisclosure may be equally applicable as technological solution(s) tomanage losses due to quality shortfall during the storage (e.g., storingmeats in a meat locker (refrigerator) at a restaurant).

One technological problem is limited ability to predict when equipment(e.g., ship's engine, etc.) involved the cargo transport might fail orto monitor and/or modify environmental conditions such as refrigeration.For example, a technological solution is wanted to predict prior toand/or during the cargo transport that a cargo transporter (e.g., truck,ship, plane) and/or cargo containers (e.g., refrigeratedcontainers/reefers, etc.) might experience an equipment failure duringtransport that would negatively impact the quality of the cargo so thatone or more remedial actions might be implemented in real-time to reduceand/or eliminate such negative impact.

Another technological problem, that might also be related to thetechnological problem identified above, is that typically the cargotransport travels along routes that have no or severely diminishedcomputer and/or communication infrastructure (e.g., open sea, desert,Canadian's Boreal forest, etc.) so that even if the cargo transportand/or cargo container is/are retrofitted with some type ofenvironmental and/or operational sensor(s), transmitting sensor dataand/or equipment operational data (e.g., telematic data) in real-time toa transport command center and/or taking real-time remedial action(s) toreduce and/or eliminate such negative impact to the quality of the cargomay be negatively impacted due to the diminished computing and/orcommunication infrastructure and/or technical limitations of equipment(e.g., sensors requiring sufficient power for long distance transmissionof the sensor/operational data).

Another technical problem is recording the specific time when a lossand/or damage may have occurred. With multiple parties involved instoring and/or transporting goods, it can be difficult to assignliability to the proper party. For example, a shipment from China to theUnited States that moves via ocean container may have a consolidationwarehouse and multiple trucks or rail carriers involved in China beforearrival at the port and then once in the United States, adeconsolidation warehouse and multiple trucks or rail carriers beforearriving at the intended destination. A technological solution is tohave a third party (for example, without limitation, a sensor-managingentity) record/determine when the loss occurred to properly assignliability to the party that may have caused the damage. This then allowsthe cargo owner or their insurance company to verifiably seekcompensation and/or subrogate against that party.

Yet another technological problem is how to timely process and generateelectronic remedial instruction(s) that affect(s) or is/are designed toaffect, for example, in real-time, the cargo transport (e.g., vehicle,ship, plane, etc.), cargo storage (e.g., refrigerated container, etc.),or both, in a positive feedback loop to reduce or eliminate the qualityshortfall of the goods (e.g., perishable goods, antiques, auto parts,etc.). For example, on one hand, the environmental and/or operationalsensor data, as detailed herein, typically might be collected/generatedby one type of equipment (e.g., data-collecting sensors), whereaselectronic remedial instruction(s), and thus communication link(s) mustbe typically established with another type of equipment (e.g., truck'selectronic computer unit (ECU)) to affect how the transport (e.g.,vehicle, ship, airplane, etc.), cargo storage (e.g., refrigeratedcontainer, etc.), or both behave/operate.

Yet another technological problem is that a typical, conventionalcomputational hardware (e.g., servers) and a typical, conventionalnetworking/transmission hardware (e.g., routers, re-transmitters,antennas, etc.) are unable to adjust their operational capabilitiesbased, for example without limitation, a type of remedial action and/ora type of transport equipment being involved.

Yet another technological problem is that a typical, conventionalcomputational hardware (e.g., servers) might not allow all stakeholdersin the cargo transport and/or storage industries to transparently accessand/or reliably verify transport and/or storage data that provides awholesome view in numerous, if not all, aspects of the cargo transport.

Another technological problem is that a typical, conventional cargoinsurance claims are not started until a loss adjuster or claimssurveyor has viewed the damage to the merchandise (e.g., at the enddestination in the case of the cargo transport, or the surveyor found anavailability (maybe in a few days or a week) to visit the restaurant toview the spoiled meat). For example, cargo transits can take over amonth in the case of ocean transport. A technological solution is tocreate parameters from the sensor derived data to determine when perilsrelated to weather, thief, mishandling, environmental stresses,equipment malfunction, fire, and/or any other potentialquality-affecting event(s) may have occurred and allowing an insurancecompany to begin and or even finalize the claim process (i.e., pay outthe compensation) while the goods are still in transit (e.g., allowingthe sender to send a new shipment when, based on the sensor derive data,the sender receives a compensation).

Another technological problem is that the typical cargo storage andtransport insurance underwriting process relies on anecdotal informationto determine policy terms and price. A technological solution is to usehistorical sensor-based and/or sensor-collected transit and storage datato automatically and dynamically manage the cargo storage and/or cargotransport insurance process lifecycles, from underwriting to claimadjudication.

BRIEF SUMMARY OF THE INVENTION

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure utilize a network of multi-functional sensors of varioustypes and related technologies to minimize and/or recoup qualityshortfall (inherent vice) of cargo (land, marine, and air), especiallyenvironmentally sensitive cargos. For example, medical drug and manyfoods shipments require very specific temperature and humidity ranges.In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure transform sensory data, such as, without limitation, a timeseries of, for example, temperature and humidity readings into inventivemetric(s) of the present disclosure that can be used to mitigate and/orrecoup quality shortfall of cargo. In some embodiments and, optionally,in combination of any embodiment described above or below, the inventivesystems and methods of the present disclosure may utilize various inputssuch as, without limitation, various scoring approaches to obtain theinventive metric(s).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure utilize a network of multi-functional sensors; where, basedat least in part on a quality insurance, each multi-functional sensor ofthe network of sensors is positioned in, positioned on, or positioned ina vicinity of at least one of: i) a transported cargo, where thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, ii) wirelessly transmit, via one or more communication modes,the cargo transport sensor data from the respective sensor to at leastone server, iii) wirelessly receive, from the at least one server, viathe one or more communication modes, one or more remedial instructionsand store the one or more remedial instructions in a second memorylocation of the respective multi-functional sensor, and iv) cause theone or more remedial instructions to be implemented with one or more of:the at least one cargo container, the at least one cargo transport, andthe at least one cargo storage; the at least one server, having cargoquality shortfall administration software stored on a non-transientcomputer readable medium; where the at least one server what is remotelylocated from the network of multi-functional sensors; where, uponexecution of the cargo quality shortfall administration software, the atleast one server is at least configured to: i) receive the cargotransport sensor data from the network of multi-functional sensors; ii)dynamically predict, based at least in part on the cargo transportsensor data, at least one current quality metric of the transportedcargo, where the at least one current quality metric is representativeof a predicted quality loss of the transported cargo from an originalcondition of the transported cargo at the at least one first geographiclocation; iii) dynamically determine, based at least in part on the atleast one current quality metric of the transported cargo and acargo-specific value remaining curve of the quality insurance, a currentloss value of the transported cargo; iv) dynamically determine, based atleast in part on cargo transport sensor data and prior to an arrival ofthe transported cargo to the at least one second geographic location,one or more remedial actions to mitigate the current loss value of thetransported cargo or avoid an additional loss value of the transportedcargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality insurance, apayout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions to one or more multi-functional sensors of thenetwork of multi-functional sensors, where the one or more remedialinstructions includes at least one adjustment to one or more of: the atleast one cargo container, the at least one cargo transport, and the atleast one cargo storage, to remedy or prevent the additional loss valueof the transport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure provides a method, including at least the following steps of:installing a network of multi-functional sensors; where, based at leastin part on a quality insurance, each multi-functional sensor of thenetwork of sensors is positioned in, positioned on, or positioned in avicinity of at least one of: i) a transported cargo, where thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, ii) wirelessly transmit, via one or more communication modes,the cargo transport sensor data from the respective sensor to at leastone server, iii) wirelessly receive, from the at least one server, viathe one or more communication modes, one or more remedial instructionsand store the one or more remedial instructions in a second memorylocation of the respective multi-functional sensor, and iv) cause theone or more remedial instructions to be implemented with one or more of:the at least one cargo container, the at least one cargo transport, andthe at least one cargo storage; receiving, by the at least one server,the cargo transport sensor data from the network of multi-functionalsensors; where the at least one server is configured to execute cargoquality shortfall administration software stored on a non-transientcomputer readable medium associated with the at least one server;dynamically predicting, by the at least one server, based at least inpart on the cargo transport sensor data, at least one current qualitymetric of the transported cargo, where the at least one current qualitymetric is representative of a predicted quality loss of the transportedcargo from an original condition of the transported cargo at the atleast one first geographic location; dynamically determining, by the atleast one server, based at least in part on the at least one currentquality metric of the transported cargo and a cargo-specific valueremaining curve of the quality insurance, a current loss value of thetransported cargo; dynamically determining, by the at least one server,based at least in part on cargo transport sensor data and prior to anarrival of the transported cargo to the at least one second geographiclocation, one or more remedial actions to mitigate the current lossvalue of the transported cargo or avoid an additional loss value of thetransported cargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality insurance, apayout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions to one or more multi-functional sensors of thenetwork of multi-functional sensors, where the one or more remedialinstructions includes at least one adjustment to one or more of: the atleast one cargo container, the at least one cargo transport, and the atleast one cargo storage, to remedy or prevent the additional loss valueof the transport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure utilize a network of multi-functional sensors; where, basedat least in part on a quality determination, each multi-functionalsensor of the network of sensors is positioned in, positioned on, orpositioned in a vicinity of at least one of: i) a transported cargo,where the transported cargo is a cargo that meets at least one of thefollowing conditions: 1) a transported condition in which thetransported cargo is transported from at least one first geographiclocation to at least one second geographic location by at least onecargo transport, or 2) a stored condition in which the transported cargois being stored in at least one cargo storage while the transportedcargo is transported from the at least one first geographic location tothe at least one second geographic location, or ii) at least one cargocontainer containing the transported cargo; where each multi-functionalsensor of the network of multi-functional sensors is configured to: i)measure at least one transport-related condition, at least onecargo-related condition, or both, to form cargo transport sensor dataand store the cargo transport sensor data in a first memory location ofa respective multi-functional sensor, ii) wirelessly transmit, via oneor more communication modes, and the cargo transport sensor data fromthe respective sensor to at least one server; the at least one server,having cargo quality shortfall administration software stored on anon-transient computer readable medium; where the at least one serverwhat is remotely located from the network of multi-functional sensors;where, upon execution of the cargo quality shortfall administrationsoftware, the at least one server is at least configured to: i) receivethe cargo transport sensor data from the network of multi-functionalsensors; ii) dynamically predict, based at least in part on the cargotransport sensor data, at least one current quality metric of thetransported cargo, where the at least one current quality metric isrepresentative of a predicted quality loss of the transported cargo froman original condition of the transported cargo at the at least one firstgeographic location; iii) dynamically determine, based at least in parton the at least one current quality metric of the transported cargo anda cargo-specific value remaining curve of the quality determination, acurrent loss value of the transported cargo; iv) dynamically determine,based at least in part on cargo transport sensor data and prior to anarrival of the transported cargo to the at least one second geographiclocation, one or more remedial actions to mitigate the current lossvalue of the transported cargo or avoid an additional loss value of thetransported cargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality determination,a payout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions, where the one or more remedial instructionsincludes at least one adjustment to one or more of: the at least onecargo container, the at least one cargo transport, and the at least onecargo storage, to remedy or prevent the additional loss value of thetransport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure provides a method, including at least the following steps of:installing a network of multi-functional sensors; where, based at leastin part on a quality determination, each multi-functional sensor of thenetwork of sensors is positioned in, positioned on, or positioned in avicinity of at least one of: i) a transported cargo, wherein thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, and ii) wirelessly transmit, via one or more communicationmodes, the cargo transport sensor data from the respective sensor to atleast one server; receiving, by the at least one server, the cargotransport sensor data from the network of multi-functional sensors;where the at least one server is configured to execute cargo qualityshortfall administration software stored on a non-transient computerreadable medium associated with the at least one server; dynamicallypredicting, by the at least one server, based at least in part on thecargo transport sensor data, at least one current quality metric of thetransported cargo, where the at least one current quality metric isrepresentative of a predicted quality loss of the transported cargo froman original condition of the transported cargo at the at least one firstgeographic location; dynamically determining, by the at least oneserver, based at least in part on the at least one current qualitymetric of the transported cargo and a cargo-specific value remainingcurve of the quality determination, a current loss value of thetransported cargo; dynamically determining, by the at least one server,based at least in part on cargo transport sensor data and prior to anarrival of the transported cargo to the at least one second geographiclocation, one or more remedial actions to mitigate the current lossvalue of the transported cargo or avoid an additional loss value of thetransported cargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality determination,a payout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions, where the one or more remedial instructionsincludes at least one adjustment to one or more of: the at least onecargo container, the at least one cargo transport, and the at least onecargo storage, to remedy or prevent the additional loss value of thetransport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one environmental condition isone of: temperature, humidity, vibration, shock, sound, light, presenceof air contaminant, pH, location, presence of at least one odor,presence of at least one gas, physical integrity of one of thetransported item or the plurality of transported items, and anycombination thereof. For example, the gas sensors may measure one ormore of ethylene, ammonia, acetylene, nitrogen, carbon dioxide, oxygen.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one computer networkcommunication is selected from the group consisting of electroniccommunications such as but limited to: NFC, RFID, Narrow Band Internetof Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA,satellite and any combination thereof.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one adjustment is a change in oneor more operational parameters of one or more of: the at least one cargotransport, the at least one cargo container, and the at least one cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one adjustment is an instructionto replace one or more of: a current cargo transport, a current cargocontainer, and a current cargo storage, with one or more of: a new cargotransport, a new cargo container, and a new cargo storage, respectively.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the one or more operational parameters of theat least one cargo transport comprise a speed of the at least one cargotransport and wherein the at least one adjustment is a change in thespeed of the at least one cargo transport.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the one or more operational parameters of theat least one cargo transport comprise a geographic direction of the atleast one cargo transport and wherein the at least one adjustment is achange in the geographic direction of the at least one cargo transport.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one server and the network ofmulti-functional sensors are associated with distinct entities.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the executing at least one remedial activityincludes generating, in real-time, at least one alert configured toinvoke at least one corrective action from the at least one transport.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one corrective action is aninstruction to change at least one transporting parameter.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, where the at least one transporting parameteris an operational parameter of the at least one transport.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one transporting parameter is achange in a transporting direction.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one server having cargo qualityshortfall administration software includes at least one machine learningalgorithm configured to determine at least one of: i) the at least onecurrent quality metric and ii) the at least one target quality metric.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one machine learning algorithm isneural network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is illustrative of computer-based system architecture forimplementing a method of the invention.

FIGS. 2A and 2B are block diagrams of exemplary sensor(s) utilized inaccordance with some embodiments of the present disclosure.

FIG. 3 is a flow diagram describing exemplary process steps associatedwith transforming sensor-collected environmental data into qualitymetrics to be utilized in accordance with at least some embodiments ofthe present disclosure.

FIG. 4 shows a sample of the type of data utilized in accordance with atleast some embodiments of the present disclosure.

FIG. 5 shows a snapshot of a graphical user interface programmed todemonstrate an illustrative model how the data is compiled andintegrated in accordance with at least some embodiments of the presentdisclosure.

FIG. 6 shows certain aspects in accordance with at least someembodiments of the present disclosure.

FIG. 7 shows a snapshot of a graphical user interface programmed todemonstrate an illustrative model how data is compiled and integrated inaccordance with at least some embodiments of the present disclosure.

FIGS. 8-10 show certain aspects in accordance with at least someembodiments of the present disclosure.

FIG. 9 shows a process flow in accordance with at least some embodimentsof the present disclosure.

FIG. 10 shows another process flow in accordance with at least someembodiments of the present disclosure.

FIGS. 11-22 show exemplary graphs illustrating certain aspects of atleast some embodiments of the present disclosure.

FIG. 23 shows an exemplary process flow for certain aspects of at leastsome embodiments of the present disclosure.

FIGS. 24A-24C show examples of sensor readings under normal and abnormalconditions requiring risk management remedial actions in accordance withat least some embodiments of the present disclosure.

FIG. 25 shows an exemplary process flow for certain aspects of at leastsome embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of a system and method foraccessing and managing structured content. Specific examples ofcomponents, processes, and implementations are described to help clarifythe invention. These are merely examples and are not intended to limitthe invention from that described in the claims. Well-known elements arepresented without detailed description so as not to obscure thepreferred embodiments of the present disclosure with unnecessary detail.For the most part, details unnecessary to obtain a completeunderstanding of the preferred embodiments of the present disclosurehave been omitted in as much as such details are within the skills ofpersons of ordinary skill in the relevant art.

FIG. 1 shows and exemplary architecture of an exemplary inventivecomputer-based system of the at least some embodiments of the presentdisclosure. In some embodiments and, optionally, in combination of anyembodiment described above or below, the exemplary inventivecomputer-based system may be configured to include hardware componentssuch as, without limitation, a collection of environmental conditionand/or goods-related condition sensors 105, internal sensor data storage110, telecommunication devices 115 and one or more processors 120 toperform the details of the premium calculations. In some embodiments,the telecommunication devices transmit the remote sensor data to centraldata storage medium 120. In some embodiments, additional hardware-baseddevices may be connected to the central storage medium to enable thedata to be processed via 130 to compute quality metrics from theenvironmental data and then stored in the central storage medium. Insome embodiments, the processors 130 execute a computer program residingin system memory 175 to perform the method. In some embodiments, videoand storage controllers 180 may be used to enable the operation ofdisplay 155. In some embodiments, the exemplary system of the presentdisclosure may include various data storage devices for data input suchas internal/external disk drives 135, internal CD/DVDs 140, tape units145, and other types of electronic storage media 150. The aforementioneddata storage devices are illustrative and exemplary only. These storagemedia are used to enter underwriting data, such as shipment values andquality metric to economic value relationships. The calculations canapply statistical software packages or can be performed from the dataentered in spreadsheet formats using Microsoft Excel, for example. Thecalculations are performed using either customized software programsdesigned for company-specific system implementations or by usingcommercially available software that is compatible with Excel or otherdatabase and spreadsheet programs. In some embodiments, the exemplarysystem of the present disclosure can also interface with proprietary orpublic external storage media 165 to link with other databases toprovide additional risk information such as weather data to the cargotransport underwriters. The output devices can be a telecommunicationdevice 170 to transmit the calculation worksheets and other systemproduced graphs and reports via cloud storage devices 170, proprietarystorage devices 165, other output media 160 such 140, 145, and 150.These output devices used herein are illustrative and exemplary only.

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” means that events and/or actionscan be triggered and/or occur without any human intervention. In someembodiments and, optionally, in combination of any embodiment describedabove or below, events and/or actions in accordance with the presentdisclosure can be in real-time and/or based on a predeterminedperiodicity of at least one of: nanosecond, several nanoseconds,millisecond, several milliseconds, second, several seconds, minute,several minutes, hourly, several hours, daily, several days, weekly,monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the inventive specially programmed computingsystems with associated devices (e.g., a network of remote sensors) areconfigured to operate in the distributed network environment,communicating over a suitable data communication network (e.g., theInternet, etc.) and utilizing at least one suitable data communicationprotocol (e.g., IPX/SPX, X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP),etc.). Of note, the embodiments described herein may, of course, beimplemented using any appropriate hardware and/or computing softwarelanguages. In this regard, those of ordinary skill in the art are wellversed in the type of computer hardware that may be used, the type ofcomputer programming techniques that may be used (e.g., object orientedprogramming), and the type of computer programming languages that may beused (e.g., C++, Objective-C, Swift, Java, JavaScript). Theaforementioned examples are, of course, illustrative and notrestrictive.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

In another form, a non-transitory article, such as a non-transitorycomputer readable medium, may be used with any of the examples mentionedabove or other examples except that it does not include a transitorysignal per se. It does include those elements other than a signal per sethat may hold data temporarily in a “transitory” fashion such as RAM andso forth. In some embodiments and, optionally, in combination of anyembodiment described above or below, the at least some embodiments ofthe present disclosure may rely on one or more distributes and/orcentralized databases (e.g., data center).

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments and, optionally, in combination of anyembodiment described above or below, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Examples of software may include software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof.Determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, exemplary sensors(s) of the present disclosuremay sense and record at least environmental attributes such as, but notlimited to, temperature, humidity, oxygen level, light, color,vibration, shock, acceleration, gyroscope data (orientation), airpressure, odors, gases (e.g. volatile organic compounds (VOC), nitrogen,ozone, CO₂), particular matters, pH, and any combination thereof. Forexample, as shown in FIG. 2A, an exemplary sensor unit of the presentdisclosure (200) may include one or more of: a temperature measuringsensor module (201) (e.g., DHT11 by Adafruit (NY, N.Y.)), humiditymeasuring sensor module (202) (e.g., DHT11 by Adafruit (NY, N.Y.)),shock measuring sensor module (203), acceleration measuring sensormodule (204), gyroscope module (205), air pressure measuring sensormodule (206), air quality measuring sensor module (e.g., particulatemonitoring)(e.g., MQ13 by Adafruit (NY, N.Y.)), magnetic sensor module(e.g., DRV5023 Digital-Switch Hall Effect Sensor, Texas InstrumentsInc., Dallas, Tex.), and/or other suitable sensor modules. The exemplarysensor unit of the present disclosure may also include a control board(not shown) configured to allow communication exchange between one ormore of the sensor modules and one or more microprocessors (207),programmed to operate the sensor modules. The exemplary sensor unit ofthe present disclosure may also include a battery (not shown). Theexemplary sensor unit of the present disclosure may also include datamemory storage (208) (e.g., an SD card).

In some embodiments, the exemplary sensor unit of the presentdisclosures may also include capturing optical parameter(s) that mayinclude at least one from a group consisting of infrared, visible, andultraviolet light parameters (e.g., TSL2561 by Adafruit (NY, N.Y.)). Forexample, without limitation, the optical detection module may include aphoto sensor to detect a level or change in level of light. In someembodiments, the optical detection may include a digital image capturedevice, such as, without limitation, a CCD or CMOS imager that capturesdata related to infrared, visible, and/or ultraviolet light images. Forexample, an exemplary light sensor can generate an indication ofincreased ambient light that may indicate the opening of a door thatshould not be open.

In some embodiments, the exemplary sensor unit of the present disclosuremay also include an acoustic detection module capturing soundparameter(s) (e.g., sound levels, sound frequencies, etc.) (e.g.,HC-SR04 by Adafruit (NY, N.Y.)).

In some embodiments, the exemplary sensor unit of the present disclosuremay also include a motion detection module capturing movement of thecargo and/or around the cargo.

The at least some embodiments of the present disclosure may utilizedistinct sensors to separately capture environmental sensor data aboutthe cargo's environment condition(s), cargo physical condition dataabout the cargo's physical condition(s), and transport equipment dataabout operational (e.g., power consumption, water consumption, etc.)and/or environmental condition(s) of the involved cargo transportequipment (e.g., ship, container, etc.).

The at least some embodiments of the present disclosure may utilizesensors that may be an active or real-time type of sensors that collect,store and forward their data in real time on a set interval or on anexception basis. In some embodiments, the active sensors of the presentdisclosure are connected to a data transmission unit (wired or wireless)to transmit and/or receive data.

The at least some embodiments of the present disclosure may utilizesensors that may be a passive type of sensors that collect and storetheir data without real-time transmission. In some embodiments, thepassive sensors of the present disclosure need to be read (wired orwireless) manually or automatically, but are not connected to a datatransmission unit.

Unless specifically called out, it is understood that the terms “sensor”and “sensors” as used herein can cover both types of sensors: the activeand the passive sensors. In some embodiments, the exemplary sensors ofthe present disclosure can have one or more functions ofsensors/monitors produced by at least one of the following companies:

1. Sensitech Inc. (Beverly Mass.),

2. DeltaTrak Inc. (Pleasanton, Calif.),

3. Elpro-Buchs AG (Switzerland),

4. Ellab A/S (Hillerod, Denmark),

5. Euroscan GmbH (Bonn, Germany),

6. Orbcomm Inc. (Rochelle Park, N.J.),

7. Coretex Limited (San Diego, Calif.),

8. Carrier eSolutions of Carrier Corp. (Syracuse, N.Y.),

9. ThermoKing (Minneapolis, Minn.), or

10. Roambee Corp. (Santa Clara, Calif.).

For example, in some embodiments, the exemplary sensor unit of thepresent disclosure (200) may include, without limitation, Chipcon'sSystem On Chip (SOC) chip (e.g., CC2530, Texas Instruments, Inc.), oneor more sensor modules, and power management module. For example, thepower supply module may provide stable 3.3V voltage for the sensormodules through the voltage regulator. For example, each sensor modulemay transfer the sensor data through one or more general-purposeinput/output (GPIO) port of the processor. For example, wireless sensornetwork wireless communication technology can use ZigBee protocol(short-range, low complexity, low power consumption, low data rate,low-cost two-way wireless communication technology or wireless networktechnology), Bluetooth, Wi-Fi and infrared and other technologies. Forexample, in some embodiments, the exemplary sensor unit of the presentdisclosure (200) may be configured to first convert, for example,humidity and/or temperature measurements into electrical signals,respectively, by, for example, a capacitive polymer humidity sensor anda temperature sensitive element made of energy gap material. Forexample, in some embodiments, the exemplary sensor unit of the presentdisclosure (200) may be configured to direct the electrical signal intothe weak signal amplifier for amplification. Next, the electrical signalenters a 14-bit A/D converter. For example, in some embodiments, theexemplary sensor unit of the present disclosure (200) may be configuredto output the digital signal through the two-wire serial digitalinterface. For example, to determine whether the goods in the carriageare safe, to judge whether the door is open, whether someone enters thecarriage, the exemplary sensor unit of the present disclosure (200) maybe configured to collect relevant data by detecting the brightness andthe infrared ray of the human body, by, for example,selecting/activating the brightness sensor and the human pyroelectricinfrared sensor.

For example, the at least some embodiments of the present disclosure mayutilize the transport equipment data to determine, in real-time, alocation specific, equipment-specific energy usage what may be furtherutilized to predict, in real-time, equipment breakdown and automaticallygenerate, in real-time, the inventive remedial cargo qualityinstruction(s) that consider the predicted equipment breakdown. Forexample, the inventive remedial cargo quality instruction(s) may beconfigured to affect the location specific level of energy usage of thespecific cargo equipment (e.g., at least one instruction to adjust atleast one of at least one operational parameter). For example, theinventive remedial cargo quality instruction(s) may identify that aserver of the cargo control center stops receiving sensor data and/orcargo transport equipment data.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, exemplary sensors(s) of the present disclosuremay sense and record any changes to physical integrity of thetransported good(s) or goods in storage (e.g., sealed condition of abottle with spirit) and/or its transporting packaging (e.g., a case/boxpackaging for transporting spirits, a pallet, a container, etc.). Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, exemplary sensors(s) of the present disclosuremay sense and record any changes affecting the expected operation of atransporting vehicle or goods in storage (e.g., ship, train, truck,warehouse, etc.). For example, at least some exemplary sensors of the atleast some embodiments of the present disclosure may be configured tostore visual and/or audio input (e.g., images of the items being shippedor stored). In some embodiments and, optionally, in combination of anyembodiment described above or below, the exemplary inventivecomputer-based system of the present disclosure may be configured toutilize the visual/audio input to dynamically and electronically track,for example, physical integrity of the transported or stored items/goods(e.g., as the items are being packed, to track that the items are ingood condition before, during, and/or after a shipment).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, in addition to the environmental attribute(s),the exemplary sensors(s) of the at least some embodiments of the presentdisclosure may record time(s) at which particular environmentalattribute(s) has/have been measured. In some embodiments and,optionally, in combination of any embodiment described above or below,in addition to the environmental attribute(s) and the correspondingtime(s), the exemplary sensors(s) of the at least some embodiments ofthe present disclosure may record at least one of: a location of asensor, location(s) at which particular environmental attribute(s)has/have been measured, and any combination thereof.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure may utilize active memory devices (e.g., electronic ormagnetic memory as opposed to barcodes, which are a passive memory) thathave additional features such as global positioning and/or environmentalsensors (e.g., temperature, humidity, vibration/shock, sound, light, aircontaminants, pH, location, odors, gases, and etc.) may also beimplemented as part of the memory devices.

For example, the memory device for a particular container may include amicroprocessor (or microcontroller) and a temperature sensor. Themicroprocessor may be configured to periodically sample the temperaturereadings from the sensor. If the temperature exceeds a predeterminedthreshold (e.g., too low or too high), then the processor may store anindication of this (e.g., the exact temperature and the time that theevent took place) in the memory device. Alternatively, the processor maybe configured to store all periodic temperature readings in the memorydevice, thereby providing the recipient and the shipping company with acomplete log of the temperatures experienced by the container throughoutthe shipping process. Taking the wireless connection one step further,the memory device may be configured with a long-range wirelesscommunications device (e.g., with a cellular or PCS telephone link,satellite link, or other wireless network protocol) to allow the memorydevice to periodically upload the temperature or other environmentalinformation and the data file to central server or to log the data inthe sensor memory for later retrieval. In embodiments where the memorydevice includes other environmental sensors, other environmental datamay be recorded in the memory device and/or transmitted via a wirelesslink. In one embodiment, central server may be included in anintelligent shipping agent system. An intelligent shipping agent may beimplemented in software configured to execute on a system of one or morecomputers coupled by a network. The software may be configured toarrange shipment and insurance for items being shipped or mailed.

Sensor device can be tamper proof to prevent resetting, unauthorizedrecalibration, or other modification by unauthorized parties as toaffect the data.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, exemplary sensors(s) of the at least someembodiments of the present disclosure may sense and/or record one ormore operational parameters of equipment (e.g., based on IP address ofsuch equipment) that is involved in the cargo transport and/or storagesuch as, without limitation, cargo transporter (e.g., truck, ship,plane) and/or cargo containers (e.g., refrigerated containers (reefers),etc.).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, as shown in FIG. 2B, the exemplary sensors(s)of the at least some embodiments of the present disclosure may beconfigured to have a dual-purpose configuration. For example, theexemplary inventive sensors of the at least some embodiments of thepresent disclosure may have at least two functionally-separate types ofhardware-software configurations (209 and 210) resided within a singlehousing (200). For example, one or more first type hardware-softwareconfigurations (209) may have dedicated memory, processing, and/orcommunication (Input/Output—I/O) capabilities to relay out the cargotransport-related sensor and/or operational data to, for example, acargo transport command center. In turn, one or more second typehardware-software configurations (210) of the same inventive sensor mayhave dedicated memory, processing, and/or communication capabilities toreceive, implement, and/or cause to implement one or more remedialinstructions (i.e., the real-time automatic positive feedback loop) thatwould result in at least one actual (e.g., physical) change in at leastone operational parameter of the equipment involved in the cargotransport to reduce and/or eliminate, in real-time, a negative impact onthe quality of the cargo that being sensed and/or about to occur basedon one or more predicted parameters as detailed herein.

In some embodiments, multiple sensors may be included in one shipment asto improve the accuracy in data measurement. In some embodiments,multiple sensors, of the same and/or different types, may be placed onthe same packaging at different places. In some embodiments, multiplesensors, of the same and/or different types, may be placed within thesame cargo container. In some embodiments, multiple sensors, of the sameand/or different types, may be placed within the same cargo storage. Insome embodiments, multiple sensors, of the same and/or different types,may be placed in a vicinity of the cargo. In some embodiments, thevicinity is from 1 millimeter to 25 meters away. In some embodiments,the vicinity is from 1 millimeter to 1 meter away. In some embodiments,the vicinity is from 1 centimeter to 1 meter away. In some embodiments,multiple sensors, of the same and/or different types, may be utilized toobtain historical data over the entire supply chain to allow the use ofsuch supply chain historical data in determining a loss history(shortage shortfall history) for any of a particular storage, aparticular shipper, a particular supplier (insured), a particularproduct, a particular mode of transport, a particular cargo container, aparticular product packaging, and any combination thereof.

In some embodiments, the exemplary inventive systems of the presentdisclosure may electronically acquire/receive one or more expertopinions regarding the entire supply chain to allow the use of suchsupply chain expert opinion data with or without any other suitable data(e.g., the supply chain historical data) in determining a lossbenchmark/standard (shortage shortfall benchmark) for any of aparticular storage, a particular shipper, a particular supplier(insured), a particular product, a particular mode of transport, aparticular cargo container, a particular product packaging, and anycombination thereof.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the term “mobile electronic device” may referto any portable electronic device that may or may not be enabled withlocation tracking functionality. For example, a mobile electronic devicecan include, but is not limited to, a mobile phone, Personal DigitalAssistant (PDA), Blackberry™, Pager, Smartphone, or any other reasonablemobile electronic device. For ease, at times the above variations arenot listed or are only partially listed; this is in no way meant to be alimitation.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the terms “proximity detection,” “locating,”“location data,” “location information,” and “location tracking” as usedherein may refer to any form of location tracking technology or locatingmethod that can be used to provide a location of, for example, aparticular sensor or other component of the exemplary inventive systemof the present disclosure, based, at least in part, on one or more ofthe following techniques, without limitation: Global Positioning Systems(GPS); GPS accessed using Bluetooth™; GPS accessed using any reasonableform of wireless and/or non-wireless communication; WiFi™ serverlocation data; Bluetooth™ based location data; triangulation such as,but not limited to, network based triangulation, WiFi™ serverinformation based triangulation, Bluetooth™ server information basedtriangulation; Cell Identification based triangulation, Enhanced CellIdentification based triangulation, Uplink-Time difference of arrival(U-TDOA) based triangulation, Time of arrival (TOA) based triangulation,Angle of arrival (AOA) based triangulation; techniques and systems usinga geographic coordinate system such as, but not limited to, longitudinaland latitudinal based, geodesic height based, Cartesian coordinatesbased; Radio Frequency Identification such as, but not limited to, Longrange RFID, Short range RFID; using any form of RFID tag such as, butnot limited to active RFID tags, passive RFID tags, battery assistedpassive RFID tags; or any other reasonable way to determine location.For ease, at times the above variations are not listed or are onlypartially listed; this is in no way meant to be a limitation.

For example, a GPS-equipped sensor may be configured to store positiondata at any granular level (e.g., item/good level, unit oftransportation level (e.g., case, pallet), contained level, transportlevel (e.g., ship, truck, etc.).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure may be configured to transmit the sensory datausing any wireless communication modes, such as, without limitation:NFC, RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitablecommunication modes.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe at present disclosure may be configured to employnear-field/frequency peer-to-peer communication (NFC) to, for examplewithout limitation, to aggregate sensory data from a network oflow-power sensors at a particular location within a transportationvehicle to an axillary transmitting device that may be programmed totransmit the sensory data over long distances by employing, for examplewithout limitation, satellite-based data transmission.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure may be configured to include a cellulartransceiver coupled to a processor and receiving a cellular networktiming signal. In some embodiments and, optionally, in combination ofany embodiment described above or below, one or more sensors and/orsensor-associated devices utilized within the exemplary inventivecomputer-based system of the present disclosure may be configured toinclude a satellite positioning receiver coupled to the processor andreceiving a satellite positioning system timing signal, and theprocessor may accordingly be configured to synchronize the internaltiming signal to the satellite positioning system timing signal as theexternal timing signal.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure may include a location module that is configuredto determine, alone or in combination with a remote server, a currentlocation of each sensor and/or the cargo transport vehicle. In someembodiments, the location module may receive cellular signals from themultiple cell towers and uses the received signals to calculate alocation of the transport vehicle. For example, the location module mayuse cellular triangulation using the multiple cell towers providing thestrongest signal to the exemplary sensor. For example, the locationmodule may determine characteristics of the cell signals and use thecharacteristics to calculate the location. For example, the locationmodule may calculate a received signal strength indication (RSSI) of thesignals and use the RSSI to calculate the location of the transportvehicle (e.g., based on a determination that stronger signals indicate acloser proximity to a known location of a cell tower, and weaker signalsindicate a farther proximity from the cell tower). In some embodiments,cellular signal characteristics other than RSSI may be used alone or incombination with RSSI.

In some embodiments, the location module may use other types oftransceivers or sensors to help determine a location of each sensor,storage, and/or transport, alone or in combination with cellularsignals. For example, a particular sensor device of the presentdisclosure may include a GPS transceiver configured to receive one ormore GPS signals and determine a position of the particular sensordevice using the signals. In some examples, a particular sensor deviceof the present disclosure may include a Wi-Fi transceiver, Bluetoothtransceiver, RFID transceiver, and/or other long or short-rangecommunication interface, and may determine its position using the datareceived via such communication interfaces. In one embodiment, aparticular sensor device of the present disclosure may use cellularsignals as a primary source for determining its position geographicallyand/or within a storage or transport, and may use location data receivedfrom other secondary sources to confirm and/or refine its locationdetermined using the cellular signals.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure may be configured to include a power source, acircuit configured to wirelessly communicate using one or more suitablecommunication protocol. In some embodiments and, optionally, incombination of any embodiment described above or below, the processor ofan exemplary NFC-enabled sensor and/or a sensor-associated device may beconfigured to synchronize an internal timing signal to an externaltiming signal, cycle power to the NFC circuit to periodically switch theNFC circuit between a peer-to-peer recognition state and a low powerstate based upon the synchronized internal timing signal, and initiatepeer-to-peer NFC communications with another NFC-enable sensor and/or aNFC-enable sensor-associated device when in range thereof and upon beingsimultaneously switched to the peer-to-peer recognition state therewith.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure may be configured to include a related physicalcomputer-readable medium and may have computer-executable instructionsfor causing a NFC-enable sensor and/or a NFC-enable sensor-associateddevice to initiating peer-to-peer NFC communications with another NFCdevice when in range thereof and upon being simultaneously switched tothe peer-to-peer recognition state therewith.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure be configured to securely store and/or transmitsensory data by utilizing one or more of encryption techniques (e.g.,private/public key pair, Triple Data Encryption Standard (3DES), blockcipher algorithms (e.g., IDEA, RC2, RCS, CAST and Skipjack),cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTRO, SHA-1,SHA-2, Tiger (TTH), WHIRLPOOL, RNGs).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the sensor data of the present disclosure maybe store in one or more private and/or private-permissionedcryptographically-protected, distributed databased such as a blockchain(distributed ledger technology), Ethereum (Ethereum Foundation, Zug,Switzerland), and/or other similar distributed data managementtechnologies. For example, as utilized herein, the distributeddatabase(s), such as distributed ledgers ensure the integrity of data bygenerating a chain of data blocks linked together by cryptographichashes of the data records in the data blocks. For example, acryptographic hash of at least a portion of data records within a firstblock, and, in some cases, combined with a portion of data records inprevious blocks is used to generate the block address for a new digitalidentity block succeeding the first block. As an update to the datarecords stored in the one or more data blocks, a new data block isgenerated containing respective updated data records and linked to apreceding block with an address based upon a cryptographic hash of atleast a portion of the data records in the preceding block. In otherwords, the linked blocks form a blockchain that inherently includes atraceable sequence of addresses that can be used to track the updates tothe data records contained therein. The linked blocks (or blockchain)may be distributed among multiple network nodes within a computernetwork such that each node may maintain a copy of the blockchain.Malicious network nodes attempting to compromise the integrity of thedatabase must recreate and redistribute the blockchain faster than thehonest network nodes, which, in most cases, is computationallyinfeasible. In other words, data integrity is guaranteed by the virtueof multiple network nodes in a network having a copy of the sameblockchain. In some embodiments, as utilized herein, a central trustauthority for sensor data management may not be needed to vouch for theintegrity of the distributed database hosted by multiple nodes in thenetwork.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, one or more sensors and/or sensor-associateddevices utilized within the exemplary inventive computer-based system ofthe present disclosure may be configured to perform a particular waybased on a type of conditioned cargo space such as, without limitation,a transport (e.g., truck, ship, airplane, etc.), a cargo storage, ahousing type enclosure used for cargo transport and/or cargo storage(e.g., dry container vs. “reefer”—type container), a short or long termstorage facility (e.g., a warehouse, storage tank, or freezer), etc.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, depending on a nature of the transporteditems/goods (e.g., perishable liquids (e.g., milk), nonperishableflammable liquids (e.g., fuel), agricultural commodities (e.g., sugar,wheat, etc.), etc.), the exemplary inventive computer-based system ofthe present disclosure may be configured to recommend at least one of:

i) type(s) of one or more sensors and/or sensor-associated devices to beused,

ii) placement location(s) of one or more sensors and/orsensor-associated devices with respect to the transported and/or storeditems/goods,

iii) mode(s) of affixing one or more sensors and/or sensor-associateddevices to the transported and/or stored items/goods.

For example, for transporting a large item (e.g., oil/gas drillingrisers), exemplary sensor-associated devices may be vibration dampeningdevices.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to process the sensory datain accordance with one or more item/good-specific protocol andpredetermined metrics, store the processed data, and transformed theprocessed sensory data into at least one of:

i) at least one alert (e.g., audible alert, visual alert, etc.) to apredetermined party (e.g., insured, shipper, etc.), and

ii) at least one visually presentation (e.g., a graph tracking, inreal-time, a quality metric based on measured time over time, etc.).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize one or morepredictive algorithms to generate risk management recommendations basedon processing sensory logistics and one or more of suitable factorsdetailed herein to prevent or mitigate loss. In some embodiments and,optionally, in combination of any embodiment described above or below,illustrative suitable factors may be, without limitation, historicalpiracy data, historical terrorist data, historical natural disasterdata, historical political risk data, weather, news (e.g., strikeannouncements), etc.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to manage a digital on-lineportal that may allow, for example without limitation, at least one ofthe following activities:

i) purchase a transportation or conditioned storage policy in real-time,and

ii) obtain stored data such as, without limitation, real-time sensorydata, historical piracy data, historical terrorist data, historicalnatural disaster data, weather, news (e.g., strike announcements),and/or etc.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to determine a historicalquality metric for a transported good/item from the historical transportdata. In some embodiments and, optionally, in combination of anyembodiment described above or below, the exemplary inventivecomputer-based system of the present disclosure may be configured tofurther transform the historical quality metrics into insurance metricparameters to dynamically determine an insurance premium associated witha particular shipment or series of shipments. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary inventive computer-based system of the present disclosuremay be configured to dynamically obtain and process the sensory data todetermine current quality metric of the shipment during the transportand/or upon a completion of at least one transport leg. In someembodiments and, optionally, in combination of any embodiment describedabove or below, the exemplary inventive computer-based system of thepresent disclosure may be configured to dynamically obtain and processthe sensory data to dynamically determine, for example withoutlimitation, a validity of sensory data, a difference between thehistorical and current quality metrics (the quality shortfall), avalidity of any claim concerning the quality shortfall, and, based onthe verified quality shortfall, dynamically determine a remedial action(e.g., payout, resale, liquidation, etc.).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure may be utilized for tracking/monitoring a physical conditionand/or environmental condition(s) that affect(s) the physical,biological, or chemical condition of numerous types of goods, including,without limitation, temperature controlled goods (TCG) which are goodssuch as, without limitation, fine art, pharmaceuticals, frozen foods,refrigerated foods, and etc. For example, cargo transport conditions(including cargo being transported and/or stored) can influence cargocommercial value of durable goods and/or pharmaceuticals. For example,in the knowledge of cargo shocks, temperature, and humidity can providevaluable insights for the owner to help reduce the uncertainty regardingcargo transport handling of fine art, certain construction materials,electronics, and textiles.

FIG. 3 is flowchart showing an illustrative example of how the collectedand compiled sensor data may be used to manage losses. In step 300, theenvironmental sensor parameters (e.g., temperature, humidity, shock,etc.) are determined/selected by, for example, based on how one or moreenvironmental characteristics may influence the quality of the cargobeing transported and/or stored. In some embodiments and, optionally, incombination of any embodiment described above or below, an exemplaryinventive quality metric may be based at least in part on a relationshipbetween the time-based sensor environmental data for a given cargo typeand effective remaining quality of the cargo.

In step 310, the score values are related to percent remaining value.These functions are mainly based on the economics and type of the cargobeing transported and/or stored. For example, for wines, excessivetemperatures erode the quality of the wine but the degradation inquality is not realized until the wine is consumed at perhaps a muchlater time than when it was shipped. In this situation, the exemplaryinventive computer-based system of the present disclosure may beconfigured to obtain and utilize a function of industry quality metric.For example, in some embodiments and, optionally, in combination of anyembodiment described above or below, the exemplary inventivecomputer-based system of the present disclosure, executing the exemplaryinventive cargo quality shortfall administration software, may beconfigured to utilize one or more wine quality metrics (e.g., winechemical composition, etc.) based on time and temperature profilesprovided, without limitation, by Jung et al., “Potential wine ageingduring transportation,” in BIO Web of Conferences 3, EDP Sciences(2014).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for perishable goods where the time toconsumption is short, temperature fluctuations can cause prematureripening which reduces or perhaps eliminates market sales. In thesesituations, the quality metric to percent remaining value may be basedat least in part on the time the cargo is expected to remain on theshelf for sale. And any reduction in that time would be reflected in apercent reduction in value.

In step 320 historical data for a given cargo type for a given qualitymetric function and a given percent remaining value equation iscompiled. In some embodiments and, optionally, in combination of anyembodiment described above or below, this data may quantify thehistorical quality shortfall performance for shipping a given cargo typethat is to be rated and priced for a given shipper.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, step 320 may be omitted in favor of receivingdata related to estimated probabilities of achieving a given qualitymetric.

In step 330, the shipping schedule is compiled. In some embodiments and,optionally, in combination of any embodiment described above or below,the shipping schedule may contain the shipper's name and shipment dataon each shipment for which the inventive cargo transport qualityshortfall administration is desired. In some embodiments and,optionally, in combination of any embodiment described above or below,the shipment data for the inventive cargo transport quality shortfalladministration may include: the source and destination, insuredfinancial value, insured quality metric, type of coverage, and thesalvage value if required.

In step 340, the exemplary quality function, the exemplary percentremaining value function, the exemplary historical quality data andexemplary coverage type are processed by the loss management and pricingplatform that may enable a user to add risk modification attributes thatare unique to the shipper into the premium calculations. In someembodiments and, optionally, in combination of any embodiment describedabove or below, the loss management model may be based at least in parton is Monte Carlo method of computational algorithms (e.g., theSolovay-Strassen type algorithms, the Baillie-PSW type algorithms, theMiller-Rabin type algorithms, and/or Schreier-Sims type algorithms) thatmay consider the historical quality data for the desiredsource-destination journeys and/or the user's experience for suchjourneys, the insured quality metrics, coverage type and/or the userentered risk modification factor(s). In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary quality function of the exemplary loss management modelmay be continuously trained by, for example without limitation, applyingat least one machine learning technique (such as, but not limited to,decision trees, boosting, support-vector machines, neural networks,nearest neighbor algorithms, Naive Bayes, bagging, random forests, etc.)to the collected and/or compiled sensor data (e.g., various type ofvisual data about environmental and/or cargo's physical/visualappearance). In some embodiments and, optionally, in combination of anyembodiment described above or below, an exemplary neutral networktechnique may be one of, without limitation, feedforward neural network,radial basis function network, recurrent neural network, convolutionalnetwork (e.g., U-net) or other suitable network. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, an exemplary implementation of Neural Network may be executed asfollows:

i) Define Neural Network architecture/model,

ii) Transfer the sensor data to the exemplary neural network model,

iii) Train the exemplary model incrementally,

iv) determine the accuracy for a specific number of timesteps,

v) apply the exemplary trained model to process the newly-receivedsensor data,

vi) optionally and in parallel, continue to train the exemplary trainedmodel with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (e.g., sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodemore or less likely to be activated.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, an exemplary connection data for eachconnection in the exemplary neural network may include at least one of anode pair or a connection weight. For example, if the exemplary neuralnetwork includes a connection from node N1 to node N2, then theexemplary connection data for that connection may include the node pair<N1, N2>. In some embodiments and, optionally, in combination of anyembodiment described above or below, the connection weight may be anumerical quantity that influences if and/or how the output of N1 ismodified before being input at N2. In the example of a recurrentnetwork, a node may have a connection to itself (e.g., the connectiondata may include the node pair <N1, N1>).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayalso include a species identifier (ID) and fitness data. For example,each species ID may indicate which of a plurality of species (e.g.,cargo's loss categories) the model is classified in. For example, thefitness data may indicate how well the exemplary trained neural networkmodel models the input sensory data set. For example, the fitness datamay include a fitness value that is determined based on evaluating thefitness function with respect to the model. For example, the exemplaryfitness function may be an objective function that is based on afrequency and/or magnitude of errors produced by testing the exemplarytrained neural network model on the input sensory data set. As a simpleexample, assume the input sensory data set includes ten rows, that theinput sensory data set includes two columns denoted A and B, and thatthe exemplary trained neural network model outputs a predicted value ofB given an input value of A. In this example, testing the exemplarytrained neural network model may include inputting each of the tenvalues of A from the input sensor data set, comparing the predictedvalues of B to the corresponding actual values of B from the inputsensor data set, and determining if and/or by how much the two predictedand actual values of B differ. To illustrate, if a particular neuralnetwork correctly predicted the value of B for nine of the ten rows,then the exemplary fitness function may assign the corresponding model afitness value of 9/10=0.9. It is to be understood that the previousexample is for illustration only and is not to be considered limiting.In some embodiments, the exemplary fitness function may be based onfactors unrelated to error frequency or error rate, such as number ofinput nodes, node layers, hidden layers, connections, computationalcomplexity, etc.

Typically, resource-constrained environments (e.g., low-power orno-power sensors) have also been considered inappropriate environmentsfor application of neural networks due to the intermittentcommunications often associated with such environments. Because acomputing device in a resource-constrained environment may operate onlow power, it may not be feasible to have an always-availablecommunications link between the resource-constrained computing device(e.g., processor of low-power or no-power sensor) and other computingdevices (e.g., the central data storage medium 120). Further, because acomputing device in a resource-constrained environment may be a low costembedded device, it may not be desirable to incur the financial cost andtechnical overhead of establishing an always-available communicationslink between the computing device and other computing devices. Further,because a computing device in a resource-constrained environment maymove around widely, it may enter areas with reduced telecommunicationsinfrastructure (e.g., lack of Wi-Fi and/or cellular networks) or areaswith no authorized telecommunications infrastructure (e.g., outside therange of recognized Wi-Fi networks). This intermittent communicationsavailability common in many resource-constrained environments has beenconsidered an impediment to deploying neural networks, at least becauseit obstructed the ability to receive training data from the environmentand then provide a trained neural network structure to the environment.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary neural network model receivesinput sensor values at input layer. In some embodiments and, optionally,in combination of any embodiment described above or below, the exemplarytrained neural network model then propagates those values throughconnections to a particular layer. In some embodiments and, optionally,in combination of any embodiment described above or below, each of theconnections may include a numerical weighting value (e.g., a valuebetween −1 and 1) that is used to modify the original value (e.g.,propagated value=original value*weight). In some embodiments and,optionally, in combination of any embodiment described above or below,nodes of the particular layer receive these propagated values as input.In some embodiments and, optionally, in combination of any embodimentdescribed above or below, each node of the particular layer may includea function that combine the received input values (e.g., summing allreceived inputs). For example, each node may further contain one or moreactivation functions that determines when a value would be output on aconnection of connections to the subsequent layer (e.g., output +1 ifthe combined value of the inputs is >0 and output −1 if the combinedvalue of the inputs is <0, and output 0 if the combined value of theinputs is =0). Each node of an exemplary output layer may correspond toa predefined category for the input sensor values. For example, thecombined input sensor values for each node of the output layer maydetermine a category determined for the input (e.g., the category forthe output node that has the largest combined input values). In someembodiments and, optionally, in combination of any embodiment describedabove or below, in this way, the exemplary neural network structure maybe used to determine a category for some sensor input.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, weights for connections may be provided withdefault and/or random values to start. In some embodiments and,optionally, in combination of any embodiment described above or below,the sensor inputs are then provided to the exemplary neural networkmodel through the input layer, and the determined categories for thesensor inputs (e.g., based on highest combined input values at the nodesof output layer) may be observed and compared to the correct categoriesas previously labeled. In some embodiments and, optionally, incombination of any embodiment described above or below, the weights forconnections may be repeatedly modified until the exemplary neuralnetwork model correctly determines the correct categories for allinputs, or at least for an acceptable portion of all inputs, to resultin the exemplary trained neural network model.

For example, when a new sensor input is received without a correctcategory previously determined, the exemplary inventive computer-basedsystem of the present disclosure may be configured to submit that inputto the exemplary trained neural network model to determine the mostlikely category for that input.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, a label applied to input sensor data may beapplied to a tuple of input data: <image, sensor data 1, sensor data 2,sensor data 3>. For example, a label provided for the input sensor datamay not be specific to just an image provided as input. Rather, thelabel may be provided as applicable to the entire situation in the cargoas described by the image, the sensor data 1, the sensor data 2, and thesensor data 3. In some embodiments and, optionally, in combination ofany embodiment described above or below, the image, sensor data 1, andsensor data 2, and sensor data 3 may all be captured in different partsof the cargo at approximately the same time. As an example, while animage input for a time t1 may show the condition of cargo at particulartemperature and humidity level to be unspoiled at time t1, then thetuple for time t1 may be labeled “no loss,” reflecting the fact that thecargo is in acceptable condition.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary neural network model may betrained in the non-resource-constrained environment using thetransferred sensor data. In some embodiments and, optionally, incombination of any embodiment described above or below, the sensor datatransferred from the resource-constrained environment may be labelledprior to or as part of the input data. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary neural network model may be further optimized by, forexample but not limited to, reducing a number of nodes, reducing anumber of connections, reducing a file size of a file storing parametersdefining the neural network model, or any combination thereof.

In step 350, the exemplary inventive computer-based system of thepresent disclosure may be configured to output a premium and execute thecargo transport quality shortfall administration for a particularshipment.

For an example of how to develop a quality metric function as depictedin FIG. 3 step 310, consider the cargo transport of the perishable good:bananas. In some embodiments and, optionally, in combination of anyembodiment described above or below, the quality metric function may bebased on bio-chemical reactions involved with the ripening of bananas asa function of temperature, humidity, and various other suitableenvironmental characteristics. The example here involves bananas grownin the tropics in Central America, packaged, and transported by cargoship to Europe via a two-week journey. For example, Kader, A. A. (2012):Banana: Recommendations for Maintaining Postharvest Quality, Producefact sheets, UC Davis, Postharvest Technology (Kader) and Kerbel, E.(2004): Banana and Plaintain, Agriculture Handbook Number 66, TheCommercial Storage of Fruits, Vegetables, and Florist and Nursery Stocks(Kerbel) have determined that the optimal storage for green bananas is13-14° C. Storage at temperatures lower than this range produceschilling injury and exposure to temperatures greater than this rangeproduces accelerated ripening. Broughton, W. J., Wu, K. F. (1979):Storage conditions and ripening of two cultivars of banana, ScientiaHorticulturae, 10, 83-93 (Broughton and Wu) have shown that the optimalrelative humidity ranges are in the range of 90 to 95%. Exposure to alower relative humidity environment also reduces bananas' green-lifeperiod. And this example demonstrates the scientific to economic valueprocess required to determine an exemplary quality metric that theexemplary inventive computer-based system of the present disclosure canutilize to execute the inventive cargo transport quality shortfalladministration for temperature variations from the optimal range (13-14°C.). A similar process may be followed for humidity.

For example, Praeger, Ulrike et. al., “Effect of Storage Climate onGreen-Life Duration of Bananas,” 5th International Workshop—Cold ChainManagement, June 2013, Bonn, Germany, have shown that the green-lifeperiod or optimal storage condition for bananas in an acceptablerelative humidity environment, 98%, varies exponentially with storagetemperature by the following equation (1):Average GreenLife Period=159.86e ^(−0.125*T)  (1),where T=storage temperature ° C.

FIG. 11 shows an exemplary graph of this equation (1) for a practical,experienced-base temperature range for the two-week ocean transportjourney.

The graph of FIG. 11 represents the scientific based data for developinga metric to represent the shipment quality associated with a givencargo. In some embodiments and, optionally, in combination of anyembodiment described above or below, one or more variables (e.g.temperature, humidity, O2/CO2 and similarly suitable others) may beanalyzed together to develop this green-life duration curve.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive quality metric may bedeveloped to represent the overall quality of the transported cargo. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive quality metric may bedesigned to indicate the effective remaining quality of the shippedcargo compared to a perfect or optimal journey environment.

In this example, since the optimal storage temperature is between 13-14°C., the exemplary inventive quality metric is taken as a ratio ofequation (2):

$\begin{matrix}{{{Banana}\mspace{14mu}{Transport}\mspace{14mu}{{Score}(T)}} = {100*\max\left\{ {1,{\frac{159.86\; e^{{- 0.125}*14}}{159.86\; e^{{- 0.125}*T}}.}} \right.}} & (2)\end{matrix}$

For example, the equation (2) may be simplified to equation (3):Banana Transport Score(T)=100*max{1,e ^(−0.1254*(T−14))}  (3).

For example, the exemplary inventive quality metric as a function of theenvironmental temperature is shown in a graph of FIG. 12.

As an example of FIG. 3 step 320, In some embodiments and, optionally,in combination of any embodiment described above or below, the exemplarypercent remaining value curve as a function of quality metric may bedeveloped based at least in part on particular attribute(s) of theshipping process and/or cargo-related inputs received from the cargo'sbuyer.

In this example, the marine transport process requires two weeks and thebananas are then distributed to various buyers by truck. FIG. 13 showsan exemplary graph of an exemplary curve that represents the remainingeconomic value as a function of the banana transport quality metric. Anexample of this function is shown in Graph 3.

The exemplary of FIG. 13 indicates a growing rate of declining value.For example, after the exemplary quality metric drops to 30 or below,shipments may be repurposed to alternative products rather than solddirectly in supermarkets at considerably less value.

Another example of developing a quality metric from sensor obtainedenvironmental data is presented for wine. In some embodiments and,optionally, in combination of any embodiment described above or below,that an exemplary quality metric for wine can be determined based atleast in part on temperatures during the shipping and/or storage. Forexample, the exemplary quality metric for wine may be determinedconsidering that the optimal storage temperature to be 12-15° C.,depending on a wine type. For example, an exemplary wine cargo is beingtransported from France to stores in South America and the total journeytime is 91 days or 13 weeks. FIG. 14 shows a graph representative oftemperature data recorded and transmitted by sensor(s) during theexemplary journey.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to capture the time-seriestemperature exposure based on the concept of degree days. In someembodiments and, optionally, in combination of any embodiment describedabove or below, other suitable methods could also be applied to capturethe temperature exposure of the shipped product, in this case, wine,from the sensor collected environmental data. For example, thetemperature degree days (from 15° C.) exposure for the exemplary winemay be computed by the following formula (45):

$\begin{matrix}{{{Temperature}\mspace{14mu}{Degree}\mspace{14mu}{Days}} = {\sum\limits_{i = 1}^{91}{{\max\left( {{T_{i} - 15},0} \right)}.}}} & (4)\end{matrix}$

For example, the illustrative calculation yields a temperature degreeday value of 162. This relatively high value was clearly caused by thetemperature excursion which occurred for 2 weeks starting at day 56. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize a metric thatquantifies the effect of this temperature excursion on wine quality. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary wine quality curves detailedherein have been developed tracking transport conditions related to oneor more wines identified in Table 1.

TABLE 1 Wine Brand/Producer Country/Region Colour Content 1 ReserveExclusive brut 0.75 Champagne Nicolas France/ W 0.75 L l FeuillatteChampagne 2 Das ist 100% Saale-Unstrut Winzervereinigung Germany/Saale W0.75 L Sauvignon Blanc Freyburg Unstrut 3 Dark Horse Chardonnay Ernest &Julio Gallo/ USA/California W 0.75 L Dark Horse 4 Capitor BordeauxSpéciale Castel/Capitor France/Pays R 0.75 L Bordeaux 5 Pinot Noir(Spätburgunder) Winery of the University Germany/ R 0.75 L red wine,Year 2016 of Geisenheim Geisenheim

For example, in cases when the wine quality erodes as a function oftemperature degree days as measured from the nominal, an exemplary idealstorage temperature may be 15° C. as depicted in a graph of FIG. 15.

To compute a quality metric for this journey, the exemplary inventivecomputer-based system of the present disclosure may be configured to use162 on the horizontal axis of the exemplary graph of FIG. 15 todetermine the corresponding quality metric of 38.

From FIG. 3, at step 320, the exemplary inventive computer-based systemof the present disclosure may be configured next step is to compute anexample of the percent remaining value associated with this journeywhere the shipment started with a starting quality metric of 100, valuedat the current market price, and has arrived at its destination with anending quality metric of 38. In some embodiments and, optionally, incombination of any embodiment described above or below, the exemplaryinventive computer-based system of the present disclosure may beconfigured to utilize the relationship between quality metric andpercent remaining value for a wine and/or wine type that may bedetermined based at least in part on one of taste tastings, priceelasticity, seller inventory turnover rates, and other suitable factorsrelated to the sales of the shipped product. For this example, theexemplary inventive computer-based system of the present disclosure maybe configured to utilize an exemplary curve shown in a graph of FIG. 16.

From the illustrative graph of FIG. 16, the exemplary quality metric of38 corresponds to about 38% remaining value.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize the exemplaryinventive sensors to obtain data related to at least one specific wineattribute and/or at least one wine characteristic category. For example,the exemplary inventive sensor(s) of the present disclosure may beconfigured to track, in real-time, chemical composition of wine in oneor more bottles by utilizing (1) one or more direct sensing methods viaat least one sensor incorporated in a bottle; and/or (2) one or moreindirect sensing methods via non-contact analyzers (e.g., Infraredchemical spectroscopy analyzer). In some embodiments and, optionally, incombination of any embodiment described above or below, the inventivetracking of the wine quality during the cargo transport and thegeneration of corresponding real-time remedial actions are based onindividual quality attribute(s) rather than on the wine as a whole. Forexample, the invention may rely on wine's quality characteristiccategories: Color, Smell, and Taste. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary inventive computer-based system of the present disclosuremay be configured to utilize the wine quality scoring that is based onnumerical scores for each class from tasting studies. FIG. 22 shows arepresentative scoring system and exemplary percent remaining valueassignments.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize the exemplarywine category quality criteria that may have been agreed between partiesto the cargo transport (e.g., shipper, insurer, insured) beforeshipments and can be utilized to generate real time remedial action(s)for the positive feedback.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize the sensorcollected journey temperature and other suitable sensor data to map thetasting results of known temperature(s) and other suitable sensor timeseries to specific scores. For example, similarly to the exemplary curveof FIG. 15 and, in at least in some embodiments, in addition todetermination of the wine quality during the transit based on FIG. 15,specific time series may be applied to test wines and with the qualityresults obtained from tastings to obtain specific score and percentremaining value curves based on wine attribute(s) instead of the overallscore.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize the sensorcollected temperature and/or other environmental conditions data toconfirm the findings of the tastings. For example, when the insurance(e.g., a remedial action) is applied only to quality degradation duringtransport due to specific environmental condition(s), the sensor datamay be used to verify that the tastings observed quality degradationsare caused by insured perils and not due to other, uninsured causes.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to provide an active,real-time positive feedback in the cargo transport of the perishablefoods based on the environmental transport quality. For example, FIG. 21shows exemplary risk factors and their effects for the bulktransportation of potatoes. In some embodiments and, optionally, incombination of any embodiment described above or below, the exemplaryinventive computer-based system of the present disclosure may beconfigured in accordance with rules defined FIG. 21. For example, items2100, 2110, 2120, 2130, 2140, and 2150 denote the transport quality riskfactors. Next to the risk factor labels, three categories are listed:Green, Yellow, and Red along with the corresponding environmentalconditions definitions. As the environmental sensor data is received inreal-time, the exemplary inventive computer-based system of the presentdisclosure may be configured to automatically calculate the score valuebased on FIG. 21 which may be representative of one of three situations:Green—no quality degradation, Yellow—partial quality degradation, orRed—major quality degradation. In step 2160, typical administrative datamay be collected and in step 2170, the exemplary inventivecomputer-based system of the present disclosure may be configured toautomatically generate, in real-time, a remedial action in a form of thecorresponding compensation, if any. In some embodiments, parties to theperishable food cargo transport may agree that one or more Yellowconditions (e.g., a certain degree of degradation) would result in acompensation of X % of insured value minus deductible and/or one or moreRed conditions would represent a claim of Y % of insured value minusdeductible.

Another example of exemplary quality metrics and percent remaining valuefunctions may be utilized for the transport of pharmaceuticals. Forexample, a particular drug Oxytocin is shipped around the world and usedto prevent and treat post-partum hemorrhage. Research typically hasshown that over a 96-day storage period, the time that it may take forOxytocin to degrade to 90% of its initial potency (“The Effect ofTemperature Changes on the Quality of Pharmaceutical Products DuringInternational Transportation,” H. Farrugia, R. Brincat, M C. Zammit, F.Wirth, A. Serracino Inglott, University of Malta, Dept. of Pharmacy,Msida, Malta) may vary according the results in Table 2.

TABLE 2 Storage Time to Degrade Temperature ° C. (days) 4 159 25 159 3064 40 35 50 16

For this example, an exemplary quality metric can be defined as theratio of the time to degrade and the initial time of 159 days. Thequality metric as a function as storage temperature for this example isshown in an exemplary graph of FIG. 17.

In this example, the value of the drug is equal to its potency and thepercent value remaining could be taken directly as the quality metricthereby eliminating the economic analysis required to develop thepercent value remaining as a function of the quality metric. In thiscase, the potency lifetime is the remaining value and there is noadditional economic analysis required. Still referencing FIG. 3, at step320, the exemplary inventive computer-based system of the presentdisclosure may be configured to take the exemplary quality metricrelated to the transport of Oxytocin as the percent value remaining. Theresultant score to value remaining function may be defined by theequation (5):percent value remaining=quality metric  (5).

FIG. 18 shows an exemplary graph that is representative of the curve ofthe exemplary equation (5).

Another example for how sensor collected environmental data can beapplied to create quality metric and percent economic value remaining,FIG. 3 steps 310 and 320, may be utilized for the inventive cargotransport quality shortfall administration for the shipment of fine art.For example, depending on the type of art being shipped temperature,humidity, shock, light, vibration, and other suitable environmental datamay be collected via an array of sensors included in the packaged art.

In this example, we consider the transport of oil-based paintings wherethe main risk exposures are shock and vibrations. Shock effects,measured in terms of g forces, are a necessary exposure associated withmovement of all cargo. For example, the transported paintings canexperience 0.3-1.3 G shocks during the road transport. For example,shocks in the range of 2-3 G can be observed each time the shipping caseis picked up or put down, for instance at customs or security checks,and illustrative shocks of 1.0-1.5 G when the case is secured in theaircraft's cabin. However, it may be possible for a case to experienceshocks in excess of 10 G from mishandling during shipment. For example,the vibrational data can be monitored at multiple frequencies. Forexample, one or more accelerometers may monitor all frequencies within agiven range (e.g. the range of the accelerometer), or a particularaccelerometer may monitor a subset of frequencies. For example, theparticular accelerometer may only monitor those frequencies associatedwith the most damage to a particular perishable good. For example, theamplitude of vibrations detected at each frequency can be monitored. Inone embodiment, a particular accelerometer may detect vibrations in anX-direction, a Y-direction, and a Z-direction. Further, the exemplaryaccelerometer may monitor both peak amplitude of measured vibrations(Gpeak), as well as an RMS value (GRMS).

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize an exemplaryfunctional relationship between shocks and fine art damage that may bedependent on one or more physical characteristics of the specific cargobeing shipped. For example, one may assume, an illustrative example,that damage may be proportional to the frequency and severity ofdiscrete shocks based at least in part on equation (6):Damage∝∫₀ ^(T) αS(t)+βS ²(t)dt  (6),where T is the total journey time, S(t) is the shock magnitude at time‘t’ of the journey, and α, β are suitable constants which may bepre-determined.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, in addition to shocks, vibrations of thepaintings during transport can cause cracking or weakening of the paintthat may crack at a later time thereby reducing the quality and value ofthe art at a later time. For example, cracks may occur according to anexemplary exponential equation (7):# of cracks formation=∫₀ ^(T) e ^(a+bx(t)+cx(t)) ² dt  (7),where T is the total journey time; a, b, and c are suitable constantswhich may be pre-determined, and x is observed vibration acceleration(m/s2) as a function of time ‘t’.

In this example there are two environmental conditions that may be usedtogether to compute an exemplary quality metric for fine art sinceeither variable can cause damage. For example, one method of computingthe quality metric for vibration and shock effects may be based at leastin part on an equation (8):Quality metric=A∫ ₀ ^(T) αS(t)+βS ²(t)dt+B∫ ₀ ^(T) e ^(a+bx(t)+cx(t)) ²dt  (8),where A and B may be normalization constants to produce scores between 0and 100.

For example, the exemplary fine art quality metric that is based onshock and vibration sensory data may be used to determine the percentremaining value. In some embodiments and, optionally, in combination ofany embodiment described above or below, for rare fine art, someshipments may produce some stress that may not be physically shown untilsometime in the future so small changes in the quality metric couldproduce long-term manifested reductions in art value, the exemplaryinventive computer-based system of the present disclosure may beconfigured to utilize a percent remaining value curve that can be basedon determinations of both the short- and long-term valuation effects ofshocks and vibration as shown in an exemplary graph of FIG. 19.

From the graph of FIG. 19, the relative sensitivity of this fine art toshocks and vibration for this example is shown. At the exemplary qualitymetric of 80, the percent remaining value is reduced to 20% of thestated value before transport.

In FIG. 4 shows a sample of a database that could be used in computingquality metric premiums as depicted in FIG. 3 step 330. Historical cargojourney data along with their associated scores can be compiled forapplication in the pricing model. In some embodiments and, optionally,in combination of any embodiment described above or below, for eachcargo shipment of a specific product, peanuts, for example, the datafields may be: source, destination, date shipped, date arrived, shipper,container type, shipment value, and resulting quality metric. This dataprovides a quantitative record of experience that may be processed bythe exemplary inventive computer-based system of the present disclosureto give users a basis for the cargo transport quality shortfalladministration such as developing quality metric shortfall and propertypremiums.

In FIG. 5, the exemplary inventive cargo transport quality shortfalladministration platform shown in FIG. 3 step 340 is presented. At step505, the underwriter enters the client name since the analysis istargeted at a specific client and their transport details; or theexemplary inventive computer-based system of the present disclosure maybe configured to automatically populate this information from data thatmay be obtained, in the past and/or in real-time, from a client'scomputer system and/or any other suitable computer source. Then in 510the journey source-destination pair from a fixed list is selected foreach shipment planned over a given period, for example one year. Thesource and destination pairs may be the results of a risk analysis ofthe historical data that groups detailed destinations into commonsources and destinations that share common risk and geographiccharacteristics. Once the Source-Destination choice is recorded, at step515, the historical fraction of shipments where the quality metric lieswithin three ranges: >85, (85-10), and 0 are displayed. These ranges arefor illustrative purpose only and other suitable ranges may be applied.The percentages give the underwriter the historical quality metricexperience associated with shipping between the source and destination.In some embodiments and, optionally, in combination of any embodimentdescribed above or below, if no historical data is available theunderwriter or analyst can enter their expert opinion on the probabilityof achieving a given score. In some embodiments and, optionally, incombination of any embodiment described above or below, even ifhistorical data exists, the underwriter can overwrite the givenpercentages to customize the historical data to the situation beingunderwritten and priced.

At step 520, the underwriter adds the insured shipment value; or theexemplary inventive computer-based system of the present disclosure maybe configured to automatically populate this information from data thatmay be obtained, in the past and/or in real-time, from a client'scomputer system and/or any other suitable computer source. This valuerepresents the insurance financial exposure for shipment and the totalinsured or shipment value is listed at step 525.

At step 530 the underwriter selects the particular type of qualitymetric insurance coverage desired: “SCORE” or “BRAND”; or the exemplaryinventive computer-based system of the present disclosure may beconfigured to automatically populate this information from data that maybe obtained, in the past and/or in real-time, from a client's computersystem and/or any other suitable computer source. For example, the SCOREcoverage identifies that if the journey quality metric is below theinsured quality metric, then the insured is paid the amount equal to theinitial shipment value times the difference in economic percentages fromthe insured score and actual score, illustrated an equation (9):Shipment value*(% Economic Value at Insured score−% Economic Value atActual Score)  (9).

As an example, using the quality metric to economic value plot shown inan exemplary graph of FIG. 13. Suppose the cargo's insured qualitymetric for a given journey was 80 and the actual quality metric was 55.The exemplary graph of FIG. 20 shows the two scores on the horizontalaxis the resulting % economic loss values on the vertical axis. Theeconomic value of the insured quality metric of 80 is 90% and the %economic value of the actual score is 70%. The claim amount for a$100,000 cargo shipment would be $100,000*(90%-70%) or $20,000.

In the BRAND coverage option, the quality metric to economic value curveis not used. BRAND coverage assumes that the cargo has lost all or mostits economic value if the quality metric falls below the insuredselected score. The claims payment is a binary calculation as shown inTable 3.

TABLE 3 ≥Insured Quality Metric Action: No claim Actual Quality {openoversize brace} <Insured Quality Metric Action: Claim = Shipment Value −Salvage Value

At step 535 the underwriter enters the insured provided quality metricfor which they desire protection; or the exemplary inventivecomputer-based system of the present disclosure may be configured toautomatically populate this information from data that may be obtained,in the past and/or in real-time, from any suitable computer source. Thisscore choice may be solely the responsibility of the insured and mayreflect their risk acceptance and their confidence in the shipper andcontainer for a given journey.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, there is no deductible explicitly used in thistype of coverage. In some embodiments and, optionally, in combination ofany embodiment described above or below, the insured essentially choosesa deductible by selecting the insured quality metric. In someembodiments and, optionally, in combination of any embodiment describedabove or below, for SCORE coverage, the decrease in economic value from100% to the value at the insured quality metric is the insureddeductible amount. In some embodiments and, optionally, in combinationof any embodiment described above or below, for BRAND coverage, thelower the insured quality metric, the insured accepts a greater loss ofperceived value even though there is no economic loss unless the actualscore is less than the insured score.

At step 540 the underwriter adds the shipment's salvage value if theshipment is insured with BRAND coverage; or the exemplary inventivecomputer-based system of the present disclosure may be configured toautomatically populate this information from data that may be obtained,in the past and/or in real-time, from any suitable computer source.

At step 545 the engineering and underwriting risk modification factorsare listed. Risk modification factors are common in insuranceunderwriting and serve to customize a given risk relative to the moregeneral historical data produced premium. In the case of pricing cargotransport quality shortfall, the specific factors listed in this stepreflect the unique risk characteristics and they may be different inother embodiments to reflect the risk modification factors that pertainto a specific type of cargo.

At step 550 the underwriter supplies their subjective insights topricing the risk by selecting the credits and debits to the modelcomputed premium associated with some or all the modification factorslisted; or the exemplary inventive computer-based system of the presentdisclosure may be configured to automatically populate this informationfrom data that may be obtained, in the past and/or in real-time, from aclient's computer system and/or any other suitable computer source. Acredit (+%) decreases the premium and a debit (−%) increases the policypremium. The summation of the risk modification factor percentages ismultiplied by the model computed premium to customize the premiumcomputed from the historical data to the specific characteristics of thepolicy being underwritten and priced.

At step 555 the underwriter enters the insured's desired deductible forstandard property insurance; or the exemplary inventive computer-basedsystem of the present disclosure may be configured to automaticallypopulate this information from data that may be obtained, in the pastand/or in real-time, from any suitable computer source. Standard cargotransport rates and deductibles may be included in the calculations withthe property premiums computed using the selected deductible andshipment values. In some embodiments and, optionally, in combination ofany embodiment described above or below, these calculations may applystandard actuarial methods that utilize company-specific rates. In someembodiments and, optionally, in combination of any embodiment describedabove or below, they may be included in the quality shortfall premiumpricing to simply provide clients a combined property and transportquality insurance package if desired.

As referenced herein, the term “quality insurance” means any insuranceproduct/service that includes, builds upon and/or is derived from anyprinciple detailed in the present description.

As referenced herein, the term “quality determination” means anydetermination that includes, builds upon and/or is derived from anyprinciple detailed in the present description related to the storage andtransport of goods (e.g., perishable goods, antiques, pharmaceuticals,automobile parts, computer parts, etc.), including, without limitation,the quality insurance.

At step 560, the underwriter enters a multiplier that transforms theloss cost-based Monte Carlo results into the full policy premium value;or the exemplary inventive computer-based system of the presentdisclosure may be configured to automatically populate this informationfrom data that may be obtained, in the past and/or in real-time, fromany suitable computer source. The multiplier reflects the additionalcharges required covering profit, expenses, claims administration, andother similar costs associated with the administration of the qualityshortfall.

At step 565, the underwriter executes the exemplary cargo transportshortfall administration platform which may run the Monte Carlo premiumcomputation.

The illustrative output of the Monte Carlo calculation is shown in FIG.6. At step 610 the total, aggregate risk distribution is shown for theunderwriter's review. It visually shows the total range of outcomes onthe horizontal axis (x) and their associated probabilities on thevertical axis (y). It is displayed as a cumulative probabilitydistribution function which indicates for any given point on the curvedenoted as (x, y), there is a ‘y’ probability, that the actual outcomewill be ‘x’ or less. And also, there is ‘(1−y)’ probability that thelosses will be greater than ‘x’.

At step 620, the average quality premium and the shipment value anddeductible-based property premium values are listed. This enables theunderwriter to price the quality shortfall coverage, property coverage,and both together. The dot on the curve of the distribution shown instep 610 also visually shows the average quality premium.

At step 630, the model computes the rate on line, a standard industrymetric by dividing the total premium charges by the total shipment valueas shown in FIG. 5 step 525. Other indicators may be used instead of, orin addition to the rate on line metric. Moreover, additional standardinsurances in addition to property insurance could also be included withthe quality metric insurance in other embodiments of this invention.

At step 640, the model computes the percentage of the quality shortfallpolicy's risk exposure or loss potential that is accepted by theunderwriter. The percentage indicates the probability that the lossescould be greater than the quality shortfall premium shown in step 620.

At step 650, the four specific loss potential percentile values arelisted. These values can help the underwriter finalize the premiumcharges and are also useful for discussions related to program or policyreinsurance issues.

As an example of how the exemplary inventive computer-based system ofthe present disclosure may be configured to administer the transportquality shortfall premium, consider the transport of high value winesfrom France to various locations around the world by a given shipper. Asshown in FIG. 3, the inputs to the underwriting and pricing model, giventhe sensor supplied time-series data, may be 1) the environmental datato quality metric function, 2) the percent remaining value versusquality metric function, and historical data for shipments with theirassociated metrics and cargo transport attributes.

Examples of the 1) and 2) have been presented in the graphs of FIGS. 15and 16 respectively and a sample of the available historical data thatmay be used is shown in FIG. 6.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, an exemplary quality metric can be computedfor each sensor-based wine storage unit and the temperature degree days(>15° C.) can be computed to produce a shipment's quality metric fromthe above plot.

Based on this curve and the anticipated sophistication of the finalbuyer of the wine, the shipper, wine owner and insurer agree on thepercent remaining value as a function of the exemplary quality metric.

To underwrite and price specific cargo shipments, the shipper may supplythe necessary shipment data as discussed in FIG. 3 step 330. With thisinformation, the exemplary inventive computer-based system of thepresent disclosure may be configured to execute the pricing model. Atthis point the pricing model contains the information as shown in FIG.7.

The Route Descriptions, Quality Metric Historical performance, ShipmentValues, the Coverage Type, Insured Quality metrics and two (2) of thenine (9) shipments require brand coverage for which the wine salvagevalues are also a part of the shipper supplied data.

At this point, the underwriter enters any risk modification factors tocustomize the pricing and the Company Multiplier, in this case set to1.856 to cover expenses, profit, etc.

After selecting the desired property insurance deductible by clicking onthe icon, the model is run to output the premium calculations as shownin FIG. 8.

In FIG. 8, the Aggregate Risk Curve shows the underwriter the full rangeof loss potentials. The dot refers to the location on the curve of the“Quality Premium” listed as the average premium. The “Property Premium”and the sum, “Total Premium” are also listed. The “Rate on Line” is the“Total Premium” divided by the “Total Shipment Value” listed in FIG. 7.The “Quality Premium Risk Acceptance” value refers to a probabilityrange from the vertical location of the dot on the curve to 100%. Itdescribes how much risk the underwriter is accepting it they use the“Quality Premium” as listed. The “Quality Premium” is the average valueof the “Aggregate Risk Distribution” shown in the plot. If theunderwriter applies the listed $99,150, there is a 42% chance the losspotential could be larger than this value.

The “Aggregate Risk Distribution” percentile values listed given someadditional quantitative information of loss potential to assist theunderwriter to deciding on the proper premium charges for this policy.The information on this part of the risk model is designed to assistunderwriters in this decision-making.

In another embodiment of this invention, fishing ships are dispersedfrom a harbor on a several day mission to fish and return to a commonunloading station. Each ship has a different storage capacity,environmental cooling capability, and speed.

At the end of the fishing period, the ships transport their delicatecargo back to the dock where they are staged to get to the dock withoutinterference from the other vessels. As time advances, the high marketvalue fish deteriorate to such an extent that they need to bere-purposed as fish oil stock at a lower market value. In someembodiments and, optionally, in combination of any embodiment describedabove or below, the exemplary inventive computer-based system of thepresent disclosure may be configured with relationships betweenenvironmental conditions and fish quality and fish value.

Environmental sensors are applied to each ship's storage hold to capturethe key parameters to measure fish quality in real time and GPS sensorsto capture vessel location and speed. The quality shortfall insurancemay be applied to the off-loaded market value of the fleet's totalcatch. The exemplary inventive computer-based system of the presentdisclosure may be configured to execute the quality shortfalladministration reflective of an insurance that may provide an insuredfloor for the total revenue from a catch.

With the environmental data to quality metric and the quality metric toremaining value functions known, the transport quality and propertyadministration platform model shown in FIG. 5 may be used. Each vessel'scatch (shipment value), the type of coverage and an insured qualitymetric are entered. The historical performance of each ship is enteredin the “Quality Metric History.” If known, otherwise it is entered fromthe underwriter's experience.

Since there is a limitation in dock space, the vessel may wait in aqueue before they can unload and during time, the catch value can erode.Based on each vessel's catch tonnage, location, and speed anoptimization program is run to ensure each vessel arrives at the dockwithout interference and the total catch revenue is maximized.

The elements of this embodiment are presented in FIG. 9. In step 900vessel installed environmental sensors may record and transmit, in nearreal time to a central server, without limitation, catch tonnage,temperatures, nitrogen content, etc.

At step 910, GPS and vessel-specific ID codes are transmitted in nearreal time to a central server.

At Step 920, the central server also loads for each vessel (or vesselID) its maximum storage capacity, maximum sustainable speed, andrefrigeration limits.

At step 930, for each vessel, the environmental conditions to qualitymetric function are loaded to the server.

At step 940, for each vessel, the quality metric to percent valueremaining function is loaded to the server.

At step 950, given each vessel's current catch, % remaining valueresults and distance from unloading, compute the current vessel andfleet level market value

At step 960, run an optimization model to provide each vessel's speed toarrive at dock to avoid waiting and to maximize the fleet level marketvalue.

At step 970, load the vessel specific data into the underwriting andpricing model to compute policy costs for cargo property insurance andinsurance for fleet-level quality shortfall.

FIG. 10 is an illustrative process flow representation as to how theinventive cargo transport quality shortfall administration may behandled throughout the lifecycle.

At step 1000, cargo shipment data is cargo transport environmentalcondition data is recorded for the total duration of the transportjourney via land, sea, air, or with any combination. One or more sensorsare attached to the cargo container to record and store environmentalconditions as a function of time. The sensors are pre-programmed torecord environmental conditions at the desired sampling rate that canvary depending on the unique requirements of the shipper and the cargo'ssensitivity to adverse conditions.

For example, if the cargo is adversely affected by temperaturesexceeding a given range for more than one hour, the sampling rate may beset at 5-minute intervals to provide the capability to signal theshipper and/or the insurer that actions need to be taken quickly toprevent a loss. As another example, if fine art truck shipment issensitive to shocks, the sampling rate could be set to 1 minute or lessto warn all interested parties that road's surface and/or the truckspeed (computed from a GPS sensor) are excessive and to direct the driveto take appropriate action. For longer, multi-week journeys traveling byship for example, sampling rates may be set to one hour or longer toconserve sensor battery life. In general, the selected sampling rate maybe based at least in part on one or more of cost of each sensor,performance characteristics, battery life, and/or data storage.

In step 1010, sensor stored raw environmental data may be periodicallytransmitted to a central location or completely stored on the sensorsand downloaded once at the end of the journey. Data transmittingfrequency may dynamically vary based at least in part on suitableeconomic characteristics, technical requirements, battery life, and/ordata storage. If a shipment is highly time sensitive, then thetransmission interval could be continuous via cellular networks as inthe case of temperature monitoring for valuable pharmaceuticals. Thedata may be collected for the complete journey: from shipment source toshipment destination.

In step 1020, the time-based environmental data is used to compute ajourney quality metric. There may be separate metrics or a combinedquality metric, for example, for temperature effects, for humidityeffects, and/or for acceleration (shock) effects. In each case, thetime-based data is processed based on an exemplary mathematicalalgorithm that computes a quality metric representing the overallenvironmental effects on the inherent quality of the cargo.

In step 1030, the transport quality metric is compared to the insuredquality metric. If the quality metric is greater than the insuredquality metric, then in step 1040, there is no claim and the contractfor this journey is closed.

If the actual quality metric is less than the insured quality metric,then assuming the policy's terms and conditions are met the coverageapplies. Next in step 1050, the type of coverage purchased by theinsured must be identified.

If the coverage type is “Score”, then the claim amount is computed instep 1060 as the insured remaining value—the actual remaining value andthe contract for the journey ends.

If the coverage type is “Brand”, then the claim amount is computed instep 1070 as the insured value—the salvage value and the contract forthe journey ends.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure, executing the exemplary inventive cargoquality shortfall administration software, may be configured todynamically manage, in real-time, various types of activities that acargo shipment may experience throughout the lifecycle of policycoverage as illustrated in FIG. 23. For example, at step 2300, the cargoleaves the seller/origin location via truck or rail car and may travelto a cargo hub for placement on board another conveyance with or withoutfurther manipulation or consolidation of additional cargoes. At step2310, the cargo leaves the cargo hub for delivery to the main port ofexport. For example, this could be airport, seaport or truck terminaland cargo may be further manipulated to better fit the cargo carryingconveyance, for example a 20′ Ocean Container, a Unit Load Device (ULD)airfreight container, or a 53-foot truck trailer. At step 2320, theillustrative cargo is on its main carrying conveyance which could be viaair, ground and/or ocean and the duration of transit could take betweenhours to over 30 days in time. At step 2330, the illustrative cargo isunloaded from its carrying conveyance at the destination place or portin reverse of the steps as outlined regarding step 2310. At step 2340,the illustrative cargo may be further deconsolidated and placed intodifferent carrying conveyances for the final delivery to thebuyer/importer. In this example, each leg of the journey may be carriedout by different transporters each with their own limitations ofliability and methods to record cargo condition. With most cargotransporters, typically, cargo condition is only recorded in writing ontransport documents and only to record the physical condition of thegoods by visual inspection. For example, typically, these records rarelyreach the seller and/or buyer unless specifically requested by themafter the journey has ended.

For example, FIGS. 24A-24C displays an exemplary environmental conditionsensor time series data plot for a cargo temperature that showsgraphically, for example, three risk management operational categories.FIG. 24A shows an exemplary plot of temperature vs. time. For example,the exemplary plot of FIG. 24A continues to grow as more temperaturedata is transmitted in time. For example, the dotted lines show thequality insurance policy-defined temperature threshold(s) that delineatewhen to generate an Alert from the normal operating region(s). Forexample, temperatures below or over the normal operating values signifyabnormal operations and remedial actions may be warranted to correctthem. For example, in the exemplary plot of FIG. 24A, the cargotransport and/or storage is operating within the insurance-definedthreshold values so no remedial action is required.

For example, FIG. 24B shows an exemplary situation where the temperaturehas exceeded the Alert's threshold value at day 14. When this occurs,for example, the exemplary inventive computer-based system of thepresent disclosure, executing the exemplary inventive cargo qualityshortfall administration software, may be configured to send via email,text, and/or phone one or more messages with one or more remedialactions to the policy holder, shipper and/or insurer (e.g., step 2410).For example, with the knowledge of a temperature issue, the shipper whois currently in custody and control of the shipment can investigate thereason(s) for the Alert and fix the problem. For example, in FIG. 24B,at step 2420, the shipper has located and fixed the issue (e.g.,replaced a part, cleaned a filter, etc.) and the temperature returned tothe normal operating range shown at step 2420. For example, the policyconditions of the exemplary quality insurance (e.g., Green, Yellow, orRed conditions of FIG. 21) show that no claim has occurred (Greencondition) so only ongoing monitoring occurs as the cargo shipmentcontinues to be transported through the sections described by FIG. 23.

FIG. 24C provides another example when the monitored cargo temperatureexceeded the Alert's threshold value again on day 14 of the journey. Forexample, in such situation, the Alert message(s) and subsequent remedialactions were unable to correct the problem and the temperature remainedin the Alert region for several weeks. In such example, based on thequality insurance policy's documented conditions, the cargo qualityentered the Yellow condition region of FIG. 21 and a claim payment wasactivated at this time at step 2440. In some embodiments, the exemplaryinventive computer-based system of the present disclosure, executing theexemplary inventive cargo quality shortfall administration software, maybe configured to cause the payment of additional payment(s) up to thepolicy limit as time advances but at the time shown at step 2440, allinterested parties have advanced knowledge of at least a partial lossand a payment is issued to the insured at this time while the cargo isstill in transport and/or storage, as outlined in the exemplary qualityinsurance policy.

FIG. 24 illustrates an exemplary process flow that the exemplaryinventive computer-based system of the present disclosure, executing theexemplary inventive cargo quality shortfall administration software, maybe configured to administer cargo shipments undergoing journeys such asshown in FIG. 23. For example, at step 2500, one or more sensors of thepresent disclosure may be attached and/or included with the cargo torecord cargo transport data (e.g., environment data, transportoperational data, etc.) at particular sampling rates that are consistentwith best practices for the particular insured cargo. At step 2510, thisinformation is periodically transmitted to, for example, a cloud serverfor storage and ongoing analysis of the incoming data either in batch ornear real time by a server running the exemplary inventive cargo qualityshortfall administration software. At step 2520, the exemplary server,running the exemplary inventive cargo quality shortfall administrationsoftware, may apply the Alert and other predictive algorithms to thecargo transport sensor data stream to, for example without limitation,analyze current out-of-specification (abnormal)situation(s)/condition(s) to measure if a particular time-basedsituation/condition warrants one or more remedial action (e.g., a claimpayment) and/or identify equipment deterioration trends and notify theappropriate maintenance personnel.

For example, at step 2530, the exemplary server, running the exemplaryinventive cargo quality shortfall administration software, may check forany cargo shipments where the environmental condition(s) is/are outsideof the Alert threshold values. For example, when the exemplary server,running the exemplary inventive cargo quality shortfall administrationsoftware, determines that the cargo transport sensor condition(s) is/arewithin normal operating ranges, then at step 2540, the exemplaryinventive cargo quality shortfall administration software, waits formore time-based sensor data to add to the analysis and continues themonitoring.

For example, when the server, running exemplary inventive cargo qualityshortfall administration software, determines that the current cargotransport sensor values are outside the normal operating range(s) of oneor more Alert threshold values, the exemplary server, running theexemplary inventive cargo quality shortfall administration software,generates an Alert event at step 2550 and checks to see if there areprevious events during particular times. In some embodiments, upondetermining of the Alert event, the exemplary server, running theexemplary inventive cargo quality shortfall administration software, maysend one or more Alert messages the interested parties and/ormulti-functional sensors of the present disclosure when, for example:

1) the temperature exceeds Alert Threshold by 5° F. or

2) the temperature has exceeded the Alert threshold value for the past“x” time intervals. For example, when the exemplary server, running theexemplary inventive cargo quality shortfall administration software,determines that the exceedance observed in Step 2530 is a new Alertevent, in Step 2550, the exemplary server, running the exemplaryinventive cargo quality shortfall administration software, mayautomatically issue one or more Alert messages (e.g., remedialinstructions) to, for example without limitation, the interested partiesand/or particular equipment (e.g., sensors, transport, storage,container) as defined, for example, in the quality insurance policy instep 2570. For example, when the exemplary server, running the exemplaryinventive cargo quality shortfall administration software, determinesthat the Alert event is not a new event, then in step 2560, theexemplary server may perform further processing, such as checkingwhether the “Red”, “Yellow”, or “Green” criteria of FIG. 21 have beenmet, to determine which remedial action to pursue. For example, when atstep 2560, the exemplary server, running the exemplary inventive cargoquality shortfall administration software, determines that a claimthreshold value (Alert threshold value) has been reached then the servermay cause a claim payment to be issued in step 2590 for the cargo'scurrent loss of value while the cargo is still in transit (includingwhile being stored during the transit). In some embodiments, theexemplary server, running the exemplary inventive cargo qualityshortfall administration software, may cause additional payments to beissued at this step as time goes on when the continued monitoring if thecargo transport sensor data shows that, for example, the environmentalconditions continue to remain outside the normal operating ranges. Forexample, when the exemplary server, running the exemplary inventivecargo quality shortfall administration software, determines that thedamage limit, as defined by the quality insurance, has not been reached,the exemplary server continues to monitor to determine whether thepreviously applied remedial actions are having any effect on bringingthe sensed environmental condition(s) back within normal operatinglimits.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure, executing the exemplary inventive cargoquality shortfall administration software, may be configured to executein one or more positive feedback loops by utilizing environments sensordata about cargo and/or transport to generate electronic remedialinstruction(s), as exemplary remedial activity, that affect(s) or is/aredesigned to affect, in real-time, how the transport (e.g., vehicle,ship, plane, etc.), cargo storage (e.g., refrigerated container, etc.),or both behave/operate to reduce or eliminate the quality shortfall ofthe transported goods (e.g., perishable goods, antiques, auto parts,etc.). For example, electronic remedial instruction(s) may slow down thetransport (e.g., reduce speed of the truck/ship) to reduce G-forces thatthe cargo may experience based on the received environmental sensor datarelated to the transported cargo. For example, electronic remedialinstruction(s) may cause a change in the movement direction of thetransport (e.g., set new GPS coordinates for ship) to avoid weatherconditions that may detrimental affect the cargo's current condition.For example, electronic remedial instruction(s) may cause a change inthe fan speed of a fan incorporated into the cargo container to increasethe air circulation inside the cardo container when, for example, duringthe transport of grains the real-time received environmental sensor datais indicative of an increased level of CO₂. As examples detailed hereinillustrate, the ability of the exemplary inventive computer-based systemto perform or cause to perform the inventive remedial feedback loopsimproves the operation of (e.g., trucks, ships, containers, storagesystems, etc.) by allowing them to maintain the quality of thetransported cargo and/or minimize decrease in the quality of thecargo—making such physical cargo transport assets smarter.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure, executing the exemplary inventive cargoquality shortfall administration software, may be configured to affect acompensation provided for any quality shortfall based ontransport/shipper/insured's ability to allow the exemplary inventivecomputer-based system to perform or cause to perform the inventiveremedial feedback loops.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the present disclosure may utilize severalaspects of at least one of:

U.S. Pat. No. 8,195,484, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,548,833, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,554,588, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,554,589, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,595,036, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,676,610, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,719,059, entitled Insurance product, rating system andmethod;

U.S. Pat. No. 8,812,331, entitled Insurance product, rating and creditenhancement system and method for insuring project savings.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure may be configured to utilize one or moremathematical functions of the aforementioned patents to transformobtained sensor recorded and stored data through cargo shipmentcontainers to remaining economic shipment values for the inventive cargotransport shortage administration. Consequently, the aforementionedpatents are incorporated by reference in their entirety for suchspecific purposes.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the aforementioned patents refer to “Savings”as tangible or intangible and include but are not limited to increasedrevenue; reduced operational expenses maintenance expenses and capitalexpenditures; increased production through-put; reduced energyconsumption; reduced emissions; increased emission credits; etc.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary inventive computer-based systemof the present disclosure, executing the exemplary inventive cargoquality shortfall administration software, may be configured toutilize 1) the received environmental sensor data, 2) the receivedoperational cargo equipment data (including, without limitation, thetelemetric data, and energy consumption data), and/or 3) the thirdparty's data reflective of a lifespan of various cargo equipment undervarious environmental and/or operational conditions (e.g.,equipment-specific historical data, current energy consumption data,breakdown frequency, etc.) to predict, in real time, cargo equipmenthealth condition (e.g., when particular cargo equipment may need to beserviced and/or would break down) and use the cargo equipment healthdata as one of the inputs for automatically generating, in real time,the remedial inventive instruction(s) that would result in reducing oreliminating the cargo's quality shortfall. For example, the real-timeremedial instruction may instruct the cargo transport to transfer cargofrom a first refrigerated container to a second refrigerated containeras soon as possible because the equipment health data for the firstrefrigerated container may predict an imminent failure of a condenser.For example, the real-time remedial instruction may instruct the cargoship to come back to the departure port or another close-by port due toa prediction that the ship's engine needs to be immediately serviced toavoid the breakdown.

In some embodiments, the exemplary sensors of the present disclosure andtheir data as collected by a third party (party not affiliated with thecargo owner, storage provider, and/or transportation provider) can beutilized, via, for example without limitation, a blockchainimplementation, to prove when a loss has occurred thereby allowing totrack and/or assignment liability and/or improve subrogation against aparty that may be negligent in causing the loss.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure utilize a network of multi-functional sensors; where, basedat least in part on a quality insurance, each multi-functional sensor ofthe network of sensors is positioned in, positioned on, or positioned ina vicinity of at least one of: i) a transported cargo, where thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, ii) wirelessly transmit, via one or more communication modes,the cargo transport sensor data from the respective sensor to at leastone server, iii) wirelessly receive, from the at least one server, viathe one or more communication modes, one or more remedial instructionsand store the one or more remedial instructions in a second memorylocation of the respective multi-functional sensor, and iv) cause theone or more remedial instructions to be implemented with one or more of:the at least one cargo container, the at least one cargo transport, andthe at least one cargo storage; the at least one server, having cargoquality shortfall administration software stored on a non-transientcomputer readable medium; where the at least one server what is remotelylocated from the network of multi-functional sensors; where, uponexecution of the cargo quality shortfall administration software, the atleast one server is at least configured to: i) receive the cargotransport sensor data from the network of multi-functional sensors; ii)dynamically predict, based at least in part on the cargo transportsensor data, at least one current quality metric of the transportedcargo, where the at least one current quality metric is representativeof a predicted quality loss of the transported cargo from an originalcondition of the transported cargo at the at least one first geographiclocation; iii) dynamically determine, based at least in part on the atleast one current quality metric of the transported cargo and acargo-specific value remaining curve of the quality insurance, a currentloss value of the transported cargo; iv) dynamically determine, based atleast in part on cargo transport sensor data and prior to an arrival ofthe transported cargo to the at least one second geographic location,one or more remedial actions to mitigate the current loss value of thetransported cargo or avoid an additional loss value of the transportedcargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality insurance, apayout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions to one or more multi-functional sensors of thenetwork of multi-functional sensors, where the one or more remedialinstructions includes at least one adjustment to one or more of: the atleast one cargo container, the at least one cargo transport, and the atleast one cargo storage, to remedy or prevent the additional loss valueof the transport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure provides a method, including at least the following steps of:installing a network of multi-functional sensors; where, based at leastin part on a quality insurance, each multi-functional sensor of thenetwork of sensors is positioned in, positioned on, or positioned in avicinity of at least one of: i) a transported cargo, where thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, ii) wirelessly transmit, via one or more communication modes,the cargo transport sensor data from the respective sensor to at leastone server, iii) wirelessly receive, from the at least one server, viathe one or more communication modes, one or more remedial instructionsand store the one or more remedial instructions in a second memorylocation of the respective multi-functional sensor, and iv) cause theone or more remedial instructions to be implemented with one or more of:the at least one cargo container, the at least one cargo transport, andthe at least one cargo storage; receiving, by the at least one server,the cargo transport sensor data from the network of multi-functionalsensors; where the at least one server is configured to execute cargoquality shortfall administration software stored on a non-transientcomputer readable medium associated with the at least one server;dynamically predicting, by the at least one server, based at least inpart on the cargo transport sensor data, at least one current qualitymetric of the transported cargo, where the at least one current qualitymetric is representative of a predicted quality loss of the transportedcargo from an original condition of the transported cargo at the atleast one first geographic location; dynamically determining, by the atleast one server, based at least in part on the at least one currentquality metric of the transported cargo and a cargo-specific valueremaining curve of the quality insurance, a current loss value of thetransported cargo; dynamically determining, by the at least one server,based at least in part on cargo transport sensor data and prior to anarrival of the transported cargo to the at least one second geographiclocation, one or more remedial actions to mitigate the current lossvalue of the transported cargo or avoid an additional loss value of thetransported cargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality insurance, apayout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions to one or more multi-functional sensors of thenetwork of multi-functional sensors, where the one or more remedialinstructions includes at least one adjustment to one or more of: the atleast one cargo container, the at least one cargo transport, and the atleast one cargo storage, to remedy or prevent the additional loss valueof the transport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure utilize a network of multi-functional sensors; where, basedat least in part on a quality insurance, each multi-functional sensor ofthe network of sensors is positioned in, positioned on, or positioned ina vicinity of at least one of: i) a transported cargo, where thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, ii) wirelessly transmit, via one or more communication modes,and the cargo transport sensor data from the respective sensor to atleast one server; the at least one server, having cargo qualityshortfall administration software stored on a non-transient computerreadable medium; where the at least one server what is remotely locatedfrom the network of multi-functional sensors; where, upon execution ofthe cargo quality shortfall administration software, the at least oneserver is at least configured to: i) receive the cargo transport sensordata from the network of multi-functional sensors; ii) dynamicallypredict, based at least in part on the cargo transport sensor data, atleast one current quality metric of the transported cargo, where the atleast one current quality metric is representative of a predictedquality loss of the transported cargo from an original condition of thetransported cargo at the at least one first geographic location; iii)dynamically determine, based at least in part on the at least onecurrent quality metric of the transported cargo and a cargo-specificvalue remaining curve of the quality insurance, a current loss value ofthe transported cargo; iv) dynamically determine, based at least in parton cargo transport sensor data and prior to an arrival of thetransported cargo to the at least one second geographic location, one ormore remedial actions to mitigate the current loss value of thetransported cargo or avoid an additional loss value of the transportedcargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality insurance, apayout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions, where the one or more remedial instructionsincludes at least one adjustment to one or more of: the at least onecargo container, the at least one cargo transport, and the at least onecargo storage, to remedy or prevent the additional loss value of thetransport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure provides a method, including at least the following steps of:installing a network of multi-functional sensors; where, based at leastin part on a quality insurance, each multi-functional sensor of thenetwork of sensors is positioned in, positioned on, or positioned in avicinity of at least one of: i) a transported cargo, wherein thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, and ii) wirelessly transmit, via one or more communicationmodes, the cargo transport sensor data from the respective sensor to atleast one server; receiving, by the at least one server, the cargotransport sensor data from the network of multi-functional sensors;where the at least one server is configured to execute cargo qualityshortfall administration software stored on a non-transient computerreadable medium associated with the at least one server; dynamicallypredicting, by the at least one server, based at least in part on thecargo transport sensor data, at least one current quality metric of thetransported cargo, where the at least one current quality metric isrepresentative of a predicted quality loss of the transported cargo froman original condition of the transported cargo at the at least one firstgeographic location; dynamically determining, by the at least oneserver, based at least in part on the at least one current qualitymetric of the transported cargo and a cargo-specific value remainingcurve of the quality insurance, a current loss value of the transportedcargo; dynamically determining, by the at least one server, based atleast in part on cargo transport sensor data and prior to an arrival ofthe transported cargo to the at least one second geographic location,one or more remedial actions to mitigate the current loss value of thetransported cargo or avoid an additional loss value of the transportedcargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality insurance, apayout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions, where the one or more remedial instructionsincludes at least one adjustment to one or more of: the at least onecargo container, the at least one cargo transport, and the at least onecargo storage, to remedy or prevent the additional loss value of thetransport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure utilize a network of multi-functional sensors; where, basedat least in part on a quality determination, each multi-functionalsensor of the network of sensors is positioned in, positioned on, orpositioned in a vicinity of at least one of: i) a transported cargo,where the transported cargo is a cargo that meets at least one of thefollowing conditions: 1) a transported condition in which thetransported cargo is transported from at least one first geographiclocation to at least one second geographic location by at least onecargo transport, or 2) a stored condition in which the transported cargois being stored in at least one cargo storage while the transportedcargo is transported from the at least one first geographic location tothe at least one second geographic location, or ii) at least one cargocontainer containing the transported cargo; where each multi-functionalsensor of the network of multi-functional sensors is configured to: i)measure at least one transport-related condition, at least onecargo-related condition, or both, to form cargo transport sensor dataand store the cargo transport sensor data in a first memory location ofa respective multi-functional sensor, ii) wirelessly transmit, via oneor more communication modes, and the cargo transport sensor data fromthe respective sensor to at least one server; the at least one server,having cargo quality shortfall administration software stored on anon-transient computer readable medium; where the at least one serverwhat is remotely located from the network of multi-functional sensors;where, upon execution of the cargo quality shortfall administrationsoftware, the at least one server is at least configured to: i) receivethe cargo transport sensor data from the network of multi-functionalsensors; ii) dynamically predict, based at least in part on the cargotransport sensor data, at least one current quality metric of thetransported cargo, where the at least one current quality metric isrepresentative of a predicted quality loss of the transported cargo froman original condition of the transported cargo at the at least one firstgeographic location; iii) dynamically determine, based at least in parton the at least one current quality metric of the transported cargo anda cargo-specific value remaining curve of the quality determination, acurrent loss value of the transported cargo; iv) dynamically determine,based at least in part on cargo transport sensor data and prior to anarrival of the transported cargo to the at least one second geographiclocation, one or more remedial actions to mitigate the current lossvalue of the transported cargo or avoid an additional loss value of thetransported cargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality determination,a payout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions, where the one or more remedial instructionsincludes at least one adjustment to one or more of: the at least onecargo container, the at least one cargo transport, and the at least onecargo storage, to remedy or prevent the additional loss value of thetransport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least some embodiments of the presentdisclosure provides a method, including at least the following steps of:installing a network of multi-functional sensors; where, based at leastin part on a quality determination, each multi-functional sensor of thenetwork of sensors is positioned in, positioned on, or positioned in avicinity of at least one of: i) a transported cargo, wherein thetransported cargo is a cargo that meets at least one of the followingconditions: 1) a transported condition in which the transported cargo istransported from at least one first geographic location to at least onesecond geographic location by at least one cargo transport, or 2) astored condition in which the transported cargo is being stored in atleast one cargo storage while the transported cargo is transported fromthe at least one first geographic location to the at least one secondgeographic location, or ii) at least one cargo container containing thetransported cargo; where each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, and ii) wirelessly transmit, via one or more communicationmodes, the cargo transport sensor data from the respective sensor to atleast one server; receiving, by the at least one server, the cargotransport sensor data from the network of multi-functional sensors;where the at least one server is configured to execute cargo qualityshortfall administration software stored on a non-transient computerreadable medium associated with the at least one server; dynamicallypredicting, by the at least one server, based at least in part on thecargo transport sensor data, at least one current quality metric of thetransported cargo, where the at least one current quality metric isrepresentative of a predicted quality loss of the transported cargo froman original condition of the transported cargo at the at least one firstgeographic location; dynamically determining, by the at least oneserver, based at least in part on the at least one current qualitymetric of the transported cargo and a cargo-specific value remainingcurve of the quality determination, a current loss value of thetransported cargo; dynamically determining, by the at least one server,based at least in part on cargo transport sensor data and prior to anarrival of the transported cargo to the at least one second geographiclocation, one or more remedial actions to mitigate the current lossvalue of the transported cargo or avoid an additional loss value of thetransported cargo, where the one or more remedial actions include: 1)instantaneously instructing to pay, based on the quality determination,a payout amount to an owner of the transported cargo to compensate thecurrent loss value of the transported cargo without a physicalinspection of the transported cargo, and 2) transmitting the one or moreremedial instructions, where the one or more remedial instructionsincludes at least one adjustment to one or more of: the at least onecargo container, the at least one cargo transport, and the at least onecargo storage, to remedy or prevent the additional loss value of thetransport cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one environmental condition isone of: temperature, humidity, vibration, shock, sound, light, presenceof air contaminant, pH, location, presence of at least one odor,presence of at least one gas, physical integrity of one of thetransported item or the plurality of transported items, and anycombination thereof.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one server is further configuredto calculate an insurance premium to be paid by the owner so that theowner is entitled to receive the payout amount.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the calculation of the insurance premium isbased at least in part on at least one of: 1) historical quality dataassociated with one or more past cargo transportations of thetransported cargo, or 2) one or more expert opinion associated with atleast one of i) the quality loss of the transported cargo or ii) anoperation of one or more of: the at least one cargo transport, the atleast one cargo container, and the at least one cargo.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one communication mode is one of,but not limited to: NFC, RFID, Narrow Band Internet of Things (NBIOT),ZigBee, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, and anycombination thereof.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the executing at least one remedial activitycomprises generating, in real-time, at least one alert configured toinvoke at least one corrective action from the at least one transport.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one corrective action is aninstruction to change at least one transporting parameter.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, wherein the at least one transportingparameter is an operational parameter of the at least one transport.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one transporting parameter is achange in a transporting direction.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the execution of the at least one remedialactivity comprises:

determining, based at least in part on the current quality metric, acurrent remaining economic value of one of the transported item or theplurality of transported items;

determining, based at least in part on the target quality metric, atarget remaining economic value of one of the transported item or theplurality of transported items; and

determining a monetary shortfall loss amount representing a differencebetween the current remaining economic value and the target remainingeconomic value; and

causing to distribute the monetary shortfall loss amount to an owner ofone of the transported item or the plurality of transported items.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one server is further configuredto calculate an insurance premium to be paid by the owner so that theowner is entitled to receive the monetary shortfall loss amount. In someembodiments and, optionally, in combination of any embodiment describedabove or below, the calculation of the insurance premium is based atleast in part on at least one historical quality metric collected forpreviously transported items.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one server having cargo qualityshortfall administration software includes at least one machine learningalgorithm configured to determine at least one of: i) the at least onecurrent quality metric and ii) the at least one target quality metric.In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the at least one machine learning algorithm isneural network.

While a number of embodiments of the present disclosure have beendescribed, it is understood that these embodiments are illustrativeonly, and not restrictive, and that many modifications may becomeapparent to those of ordinary skill in the art, including that theinventive methodologies, the inventive systems, and the inventivedevices described herein can be utilized in any combination with eachother. Further still, the various steps may be carried out in anydesired order (and any desired steps may be added and/or any desiredsteps may be eliminated).

The invention claimed is:
 1. A system, comprising: a network ofmulti-functional sensors; wherein, based at least in part on a qualityinsurance, each multi-functional sensor of the network of sensors ispositioned in, positioned on, or positioned in a vicinity of at leastone of: i) a transported cargo, wherein the transported cargo is a cargothat meets at least one of the following conditions: 1) a transportedcondition in which the transported cargo is transported from at leastone first geographic location to at least one second geographic locationby at least one cargo transport, or 2) a stored condition in which thetransported cargo is being stored in at least one cargo storage whilethe transported cargo is transported from the at least one firstgeographic location to the at least one second geographic location, orii) at least one cargo container containing the transported cargo;wherein each multi-functional sensor of the network of multi-functionalsensors is configured to: i) measure, over at least one time period, aplurality of measurements of at least one transport-related condition,at least one cargo-related condition, or both, to form cargo transportsensor data and store the cargo transport sensor data in a first memorylocation of a respective multi-functional sensor, ii) wirelesslytransmit, via one or more communication modes, the cargo transportsensor data from the respective sensor to at least one server, iii)wirelessly receive, from the at least one server, via the one or morecommunication modes, one or more remedial instructions and store the oneor more remedial instructions in a second memory location of therespective multi-functional sensor, and iv) implement the one or moreremedial instructions, by communicating at least one real-timeadjustment to at least one other device associated with one or more of:the at least one cargo container, the at least one cargo transport, andthe at least one cargo storage, to result in an at least one operationalchange of one or more of: the at least one cargo container, the at leastone cargo transport, and the at least one cargo storage; the at leastone server, having cargo quality shortfall administration softwarestored on a non-transient computer readable medium; wherein the at leastone server is remotely located from the network of multi-functionalsensors; wherein, upon execution of the cargo quality shortfalladministration software, the at least one server is at least configuredto: i) receive the cargo transport sensor data from the network ofmulti-functional sensors; ii) dynamically predict a predicted remainingquality score of the transported cargo from an original condition of thetransported cargo at the at least one first geographic location, basedat least in part on the plurality of measurements of the cargo transportsensor data measured over the at least one time period and at least oneof: a) Euler's number, e, or b) at least one item attribute of at leastone item in the transported cargo and at least one item characteristiccategory of the at least one item in the transported cargo; iii)dynamically determine, based at least in part on the predicted remainingquality score of the transported cargo and a non-linear cargo-specificremaining monetary value curve of the quality insurance, a currentremaining monetary value of the transported cargo; iv) dynamicallydetermine, based at least in part on cargo transport sensor data andprior to an arrival of the transported cargo to the at least one secondgeographic location, one or more remedial actions chosen fromcompensating for a reduction in an initial monetary value of thetransported cargo, mitigating a reduction in the current remainingmonetary value of the transported cargo and avoiding the reduction inthe current remaining monetary value of the transported cargo; whereinthe remedial action of compensating for the reduction in the initialmonetary value of the transported cargo comprises: instantaneouslyinstructing, prior to the arrival of the transported cargo to the atleast one second geographic location, to pay, based on the qualityinsurance and without the physical inspection of the transported cargo,a payout amount to an owner of the transported cargo, wherein the payoutamount is equal to a product of: 1) the initial monetary value of thetransported cargo at the at least one first geographic location and 2) adifference between:  a) a first value corresponding to a first percentmonetary value of the transported cargo at an insured quality score ofthe transported cargo and  b) a second value corresponding to a secondpercent monetary value of the transported cargo at the predictedremaining quality score of the transported cargo.
 2. The system of claim1, wherein the at least one transport-related condition or the at leastone cargo-related condition is chosen from: temperature, humidity,vibration, shock, sound, light, presence of air contaminant, pH,location, presence of at least one odor, presence of at least one gas,physical integrity, and any combination thereof.
 3. The system of claim1, wherein the one or more communication modes comprise: NFC, RFID,NBIOT, ZigBee, B3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, andany combination thereof.
 4. The system of claim 1, wherein the one ormore remedial actions further comprise transmitting, in accordance withthe quality insurance, the one or more remedial instructions to one ormore multi-functional sensors of the network of multi-functional sensorsto remedy or prevent the reduction in the current remaining monetaryvalue of the transport cargo; and wherein the at least one real-timeadjustment is a change in one or more operational parameters of one ormore of: the at least one cargo transport, the at least one cargocontainer, and the at least one cargo.
 5. The system of claim 1, whereinthe one or more remedial actions further comprise transmitting, inaccordance with the quality insurance, the one or more remedialinstructions to one or more multi-functional sensors of the network ofmulti-functional sensors to remedy or prevent the reduction in thecurrent remaining monetary value of the transport cargo; and wherein theat least one real-time adjustment is an instruction to replace one ormore of: a current cargo transport, a current cargo container, and acurrent cargo storage, with one or more of: a new cargo transport, a newcargo container, and a new cargo storage, respectively.
 6. The system ofclaim 4, wherein the one or more operational parameters of the at leastone cargo transport comprise a speed of the at least one cargo transportand wherein the at least one adjustment is a change in the speed of theat least one cargo transport.
 7. The system of claim 4, wherein the oneor more operational parameters of the at least one cargo transportcomprise a geographic direction of the at least one cargo transport andwherein the at least one real-time adjustment is a change in thegeographic direction of the at least one cargo transport.
 8. The systemof claim 1, wherein the at least one server and the network ofmulti-functional sensors are associated with distinct entities.
 9. Thesystem of claim 1, wherein the at least one server is further configuredto calculate an insurance premium to be paid by the owner so that theowner is entitled to receive the payout amount.
 10. The system of claim9, wherein the calculation of the insurance premium is based at least inpart on at least one chosen from: 1) historical quality data associatedwith one or more past cargo transportations of the transported cargo, or2) one or more expert opinion associated with at least one of i) aneffect of at least one transport-related condition on the at least onecargo-related condition or ii) an operation of one or more of: the atleast one cargo transport, the at least one cargo container, and the atleast one cargo.
 11. The system of claim 1, wherein the at least oneserver having cargo quality shortfall administration software comprisesat least one machine learning algorithm configured to dynamicallypredict the predicted remaining quality score of the transported cargo.12. The system of claim 11, wherein the at least one machine learningalgorithm is a neural network.
 13. A method, comprising: installing anetwork of multi-functional sensors; wherein, based at least in part ona quality insurance, each multi-functional sensor of the network ofsensors is positioned in, positioned on, or positioned in a vicinity ofat least one of: i) a transported cargo, wherein the transported cargois a cargo that meets at least one of the following conditions: 1) atransported condition in which the transported cargo is transported fromat least one first geographic location to at least one second geographiclocation by at least one cargo transport, or 2) a stored condition inwhich the transported cargo is being stored in at least one cargostorage while the transported cargo is transported from the at least onefirst geographic location to the at least one second geographiclocation, or ii) at least one cargo container containing the transportedcargo; wherein each multi-functional sensor of the network ofmulti-functional sensors is configured to: i) measure, over at least onetime period, a plurality of measurements of at least onetransport-related condition, at least one cargo-related condition, orboth, to form cargo transport sensor data and store the cargo transportsensor data in a first memory location of a respective multi-functionalsensor, ii) wirelessly transmit, via one or more communication modes,the cargo transport sensor data from the respective sensor to at leastone server, iii) wirelessly receive, from the at least one server, viathe one or more communication modes, one or more remedial instructionsand store the one or more remedial instructions in a second memorylocation of the respective multi-functional sensor, and iv) implementthe one or more remedial instructions, by communicating at least onereal-time adjustment to at least one other device associated with one ormore of: the at least one cargo container, the at least one cargotransport, and the at least one cargo storage, to result in anoperational change of one or more of: the at least one cargo container,the at least one cargo transport, and the at least one cargo storage;receiving, by the at least one server, the cargo transport sensor datafrom the network of multi-functional sensors; wherein the at least oneserver is configured to execute cargo quality shortfall administrationsoftware stored on a non-transient computer readable medium associatedwith the at least one server; dynamically predicting, by the at leastone server, a predicted remaining quality score of the transported cargofrom an original condition of the transported cargo at the at least onefirst geographic location based at least in part on the plurality ofmeasurements of the cargo transport sensor data measured over the atleast one time period and at least one of: a) Euler's number, e, or b)at least one item attribute of at least one item in the transportedcargo and at least one item characteristic category of the at least oneitem in the transported cargo; dynamically determining, by the at leastone server, based at least in part on the predicted remaining qualityscore of the transported cargo and a non-linear cargo-specific remainingmonetary value curve of the quality insurance, a current remainingmonetary value of the transported cargo; dynamically determining, by theat least one server, based at least in part on cargo transport sensordata and prior to an arrival of the transported cargo to the at leastone second geographic location, one or more remedial actions chosen fromcompensating for a reduction in an initial monetary value of thetransported cargo, mitigating a reduction in the current remainingmonetary value of the transported cargo and avoiding the reduction inthe current remaining monetary value of the transported cargo; whereinthe remedial action of compensating for the reduction in the initialmonetary value of the transported cargo comprises: instantaneouslyinstructing, prior to the arrival of the transported cargo to the atleast one second geographic location, to pay, based on the qualityinsurance and without the physical inspection of the transported cargo,a payout amount to an owner of the transported cargo, wherein the payoutamount is equal to a product of: 1) the initial monetary value of thetransported cargo at the at least one first geographic location and 2) adifference between: a) a first value corresponding to a first percentmonetary value of the transported cargo at an insured quality score ofthe transported cargo and b) a second value corresponding to a secondpercent monetary value of the transported cargo at the predictedremaining quality score of the transported cargo.
 14. The method ofclaim 13, wherein the at least one transport-related condition or the atleast one cargo-related condition is chosen from: temperature, humidity,vibration, shock, sound, light, presence of air contaminant, pH,location, presence of at least one odor, presence of at least one gas,physical integrity, and any combination thereof.
 15. The method of claim13, wherein the one or more communication modes comprise: NFC, RFID,NBIOT, ZigBee, B3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, andany combination thereof.
 16. The method of claim 13, wherein the one ormore remedial actions further comprise transmitting, in accordance withthe quality insurance, the one or more remedial instructions to one ormore multi-functional sensors of the network of multi-functional sensorsto remedy or prevent the reduction in the current remaining monetaryvalue of the transport cargo; and wherein the at least one real-timeadjustment is a change in one or more operational parameters of one ormore of: the at least one cargo transport, the at least one cargocontainer, and the at least one cargo storage.
 17. The method of claim13, wherein the one or more remedial actions further comprisetransmitting, in accordance with the quality insurance, the one or moreremedial instructions to one or more multi-functional sensors of thenetwork of multi-functional sensors to remedy or prevent the reductionin the current remaining monetary value of the transport cargo; andwherein the at least one real-time adjustment is an instruction toreplace one or more of: a current cargo transport, a current cargocontainer, and a current cargo storage, with one or more of: a new cargotransport, a new cargo container, and a new cargo storage, respectively.18. The method of claim 16, wherein the one or more operationalparameters of the at least one cargo transport comprise a speed of theat least one cargo transport and wherein the at least one adjustment isa change in the speed of the at least one cargo transport.
 19. Themethod of claim 16, wherein the one or more operational parameters ofthe at least one cargo transport comprise a geographic direction of theat least one cargo transport and wherein the at least one real-timeadjustment is a change in the geographic direction of the at least onecargo transport.
 20. The method of claim 13, wherein the at least oneserver and the network of multi-functional sensors are associated withdistinct entities.
 21. The method of claim 13, wherein the at least oneserver is further configured to calculate an insurance premium to bepaid by the owner so that the owner is entitled to receive the payoutamount.
 22. The method of claim 21, wherein the calculation of theinsurance premium is based at least in part on at least one chosenfrom: 1) historical quality data associated with one or more past cargotransportations of the transported cargo, or 2) one or more expertopinion associated with at least one of i) an effect of at least onetransport-related condition on the at least one cargo-related conditionor ii) an operation of one or more of: the at least one cargo transport,the at least one cargo container, and the at least one cargo.
 23. Themethod of claim 13, wherein the at least one server having cargo qualityshortfall administration software comprises at least one machinelearning algorithm configured to dynamically predict the predictedremaining quality score of the transported cargo.
 24. The method ofclaim 23, wherein the at least one machine learning algorithm is aneural network.
 25. A system, comprising: a network of multi-functionalsensors; wherein, based at least in part on a quality determination,each multi-functional sensor of the network of sensors is positioned in,positioned on, or positioned in a vicinity of at least one of: i) atransported cargo, wherein the transported cargo is a cargo that meetsat least one of the following conditions: 1) a transported condition inwhich the transported cargo is transported from at least one firstgeographic location to at least one second geographic location by atleast one cargo transport, or 2) a stored condition in which thetransported cargo is being stored in at least one cargo storage whilethe transported cargo is transported from the at least one firstgeographic location to the at least one second geographic location, orii) at least one cargo container containing the transported cargo;wherein each multi-functional sensor of the network of multi-functionalsensors is configured to: i) measure, over at least one time period, aplurality of measurements of at least one transport-related condition,at least one cargo-related condition, or both, to form cargo transportsensor data and store the cargo transport sensor data in a memorylocation of a respective multi-functional sensor, and ii) wirelesslytransmit, via one or more communication modes, the cargo transportsensor data from the respective sensor to at least one server; the atleast one server, having cargo quality shortfall administration softwarestored on a non-transient computer readable medium; wherein the at leastone server is remotely located from the network of multi-functionalsensors; wherein, upon execution of the cargo quality shortfalladministration software, the at least one server is at least configuredto: i) receive the cargo transport sensor data from the network ofmulti-functional sensors; ii) dynamically predict a predicted remainingquality score of the transported cargo from an original condition of thetransported cargo at the at least one first geographic location, basedat least in part on the plurality of measurements of the cargo transportsensor data measured over the at least one time period and at least oneof: a) Euler's number, e, or b) at least one item attribute of at leastone item in the transported cargo and at least one item characteristiccategory of the at least one item in the transported cargo; iii)dynamically determine, based at least in part on the predicted remainingquality score of the transported cargo and a non-linear cargo-specificremaining monetary value curve of the quality insurance, a currentremaining monetary value of the transported cargo; iv) dynamicallydetermine, based at least in part on cargo transport sensor data andprior to an arrival of the transported cargo to the at least one secondgeographic location, one or more remedial actions chosen fromcompensating for a reduction in an initial monetary value of thetransported cargo, mitigating a reduction in the current remainingmonetary value of the transported cargo and avoiding the reduction inthe current remaining monetary value of the transported cargo; whereinthe remedial action of compensating for the reduction in the initialmonetary value of the transported cargo comprises: instantaneouslyinstructing, prior to the arrival of the transported cargo to the atleast one second geographic location, to pay, based on the qualityinsurance and without the physical inspection of the transported cargo,a payout amount to an owner of the transported cargo, wherein the payoutamount is equal to a product of: 1) the initial monetary value of thetransported cargo at the at least one first geographic location and 2) adifference between:  a) a first value corresponding to a first percentmonetary value of the transported cargo at an insured quality score ofthe transported cargo and  b) a second value corresponding to a secondpercent monetary value of the transported cargo at the predictedremaining quality score of the transported cargo.
 26. A method,comprising: installing a network of multi-functional sensors; wherein,based at least in part on a quality determination, each multi-functionalsensor of the network of sensors is positioned in, positioned on, orpositioned in a vicinity of at least one of: i) a transported cargo,wherein the transported cargo is a cargo that meets at least one of thefollowing conditions: 1) a transported condition in which thetransported cargo is transported from at least one first geographiclocation to at least one second geographic location by at least onecargo transport, or 2) a stored condition in which the transported cargois being stored in at least one cargo storage while the transportedcargo is transported from the at least one first geographic location tothe at least one second geographic location, or ii) at least one cargocontainer containing the transported cargo; wherein eachmulti-functional sensor of the network of multi-functional sensors isconfigured to: i) measure, over at least one time period, a plurality ofmeasurements of at least one transport-related condition, at least onecargo-related condition, or both, to form cargo transport sensor dataand store the cargo transport sensor data in a first memory location ofa respective multi-functional sensor, and ii) wirelessly transmit, viaone or more communication modes, the cargo transport sensor data fromthe respective sensor to at least one server; receiving, by at least oneserver, the cargo transport sensor data from the network ofmulti-functional sensors; wherein the at least one server is configuredto execute cargo quality shortfall administration software stored on anon-transient computer readable medium associated with the at least oneserver; dynamically predicting, by the at least one server, a predictedremaining quality score of the transported cargo from an originalcondition of the transported cargo at the at least one first geographiclocation based at least in part on the plurality of measurements of thecargo transport sensor data measured over the at least one time periodand at least one of: a) Euler's number, e, or b) at least one itemattribute of at least one item in the transported cargo and at least oneitem characteristic category of the at least one item in the transportedcargo; dynamically determining, by the at least one server, based atleast in part on the predicted remaining quality score of thetransported cargo and a non-linear cargo-specific remaining monetaryvalue curve of the quality insurance, a current remaining monetary valueof the transported cargo; dynamically determining, by the at least oneserver, based at least in part on cargo transport sensor data and priorto an arrival of the transported cargo to the at least one secondgeographic location, one or more remedial actions chosen fromcompensating for a reduction in an initial monetary value of thetransported cargo, mitigating a reduction in the current remainingmonetary value of the transported cargo and avoiding the reduction inthe current remaining monetary value of the transported cargo; whereinthe remedial action of compensating for the reduction in the initialmonetary value of the transported cargo comprises: instantaneouslyinstructing, prior to the arrival of the transported cargo to the atleast one second geographic location, to pay, based on the qualityinsurance and without the physical inspection of the transported cargo,a payout amount to an owner of the transported cargo, wherein the payoutamount is equal to a product of: 1) the initial monetary value of thetransported cargo at the at least one first geographic location and 2) adifference between: a) a first value corresponding to a first percentmonetary value of the transported cargo at an insured quality score ofthe transported cargo and b) a second value corresponding to a secondpercent monetary value of the transported cargo at the predictedremaining quality score of the transported cargo.